Patent Publication Number: US-7917215-B2

Title: Closed loop cardiac resynchronization therapy using cardiac activation sequence information

Description:
RELATED PATENT DOCUMENTS 
     This application is a continuation of U.S. patent application Ser. No. 11/125,068 filed on May 9, 2005, to issue as U.S. Pat. No. 7,457,664 on Nov. 25, 2008, to which Applicant claims priority under 35 U.S.C. §120, and which is incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to implantable medical devices employing cardiac signal separation and, more particularly, to cardiac sensing and/or stimulation devices employing cardiac activation sequence monitoring and tracking for closed-loop cardiac resynchronization therapy. 
     BACKGROUND OF THE INVENTION 
     The healthy heart produces regular, synchronized contractions. Rhythmic contractions of the heart are normally initiated by the sinoatrial (SA) node, which is a group of specialized cells located in the upper right atrium. The SA node is the normal pacemaker of the heart, typically initiating 60-100 heartbeats per minute. When the SA node is pacing the heart normally, the heart is said to be in normal sinus rhythm. 
     If the heart&#39;s electrical activity becomes uncoordinated or irregular, the heart is denoted to be arrhythmic. Cardiac arrhythmia impairs cardiac efficiency and may be a potential life-threatening event. Cardiac arrhythmias have a number of etiological sources, including tissue damage due to myocardial infarction, infection, or degradation of the heart&#39;s ability to generate or synchronize the electrical impulses that coordinate contractions. 
     Bradycardia occurs when the heart rhythm is too slow. This condition may be caused, for example, by impaired function of the SA node, denoted sick sinus syndrome, or by delayed propagation or blockage of the electrical impulse between the atria and ventricles. Bradycardia produces a heart rate that is too slow to maintain adequate circulation. 
     When the heart rate is too rapid, the condition is denoted tachycardia. Tachycardia may have its origin in either the atria or the ventricles. Tachycardias occurring in the atria of the heart, for example, include atrial fibrillation and atrial flutter. Both conditions are characterized by rapid contractions of the atria. Besides being hemodynamically inefficient, the rapid contractions of the atria may also adversely affect the ventricular rate. 
     Ventricular tachycardia occurs, for example, when electrical activity arises in the ventricular myocardium at a rate more rapid than the normal sinus rhythm. Ventricular tachycardia may quickly degenerate into ventricular fibrillation. Ventricular fibrillation is a condition denoted by extremely rapid, uncoordinated electrical activity within the ventricular tissue. The rapid and erratic excitation of the ventricular tissue prevents synchronized contractions and impairs the heart&#39;s ability to effectively pump blood to the body, which is a fatal condition unless the heart is returned to sinus rhythm within a few minutes. 
     Implantable cardiac rhythm management systems have been used as an effective treatment for patients with serious arrhythmias, as well as for patients with conditions such as heart failure. These systems typically include one or more leads and circuitry to sense signals from one or more interior and/or exterior surfaces of the heart. Such systems also include circuitry for generating electrical pulses that are applied to cardiac tissue at one or more interior and/or exterior surfaces of the heart. For example, leads extending into the patient&#39;s heart are connected to electrodes that contact the myocardium for sensing the heart&#39;s electrical signals and for delivering pulses to the heart in accordance with various therapies for treating arrhythmias. 
     Typical implantable cardioverter/defibrillators include one or more endocardial leads to which at least one defibrillation electrode is connected. Such implantable cardioverter/defibrillators are capable of delivering high-energy shocks to the heart, interrupting the ventricular tachyarrhythmia or ventricular fibrillation, and allowing the heart to resume normal sinus rhythm. Implantable cardioverter/defibrillators may also include pacing functionality. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to cardiac monitoring and/or stimulation methods and systems that provide monitoring, diagnosing, defibrillation therapies, pacing therapies, or a combination of these capabilities, including cardiac systems incorporating or working in cooperation with neuro-stimulating devices, drug pumps, or other therapies. Embodiments of the present invention relate generally to implantable medical devices employing cardiac signal separation and, more particularly, to cardiac monitoring and/or stimulation devices employing automated cardiac activation sequence monitoring and/or tracking for closed-loop cardiac resynchronization therapy. 
     Embodiments of the invention are directed to devices and methods that involve the sensing of composite cardiac signals using a implantable electrodes. A source separation is performed using the sensed composite cardiac signals, producing one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences. A cardiac resynchronization therapy is adjusted using one or both of the one or more cardiac signal vectors and the signals associated with the one or more cardiac signal vectors. 
     In further embodiments, the cardiac resynchronization therapy may be initiated, terminated, or one or more parameters of the resynchronization therapy may be altered. In specific embodiments, an angle of the one or more cardiac signal vectors may be determined, wherein adjusting the cardiac resynchronization therapy may be based on the determined angle. For example, a dominant orientation of the QRS vector may be moved in a direction of an angle of a baseline QRS vector by adjusting one or more parameters of the cardiac resynchronization therapy. 
     An embodiment of a cardiac system in accordance with the present invention includes implantable electrodes configured for sensing a composite signal, thereby providing composite signals. A housing, configured for implantation in a patient, includes a controller coupled to the implantable electrodes. A memory may be provided in the housing and coupled to the controller, or may be provided in a patient-external device and coupled to the controller, such as by using wireless communications. The controller is configured to perform a source separation using the sensed plurality of composite signals, the source separation producing one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences, and to providing a closed-loop control of a cardiac resynchronization therapy using the one or more cardiac signal vectors. 
     The above summary of the present invention is not intended to describe each embodiment or every implementation of the present invention. Advantages and attainments, together with a more complete understanding of the invention, will become apparent and appreciated by referring to the following detailed description and claims taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A and 1B  are pictorial diagrams of an electrocardiogram (ECG) waveform for three consecutive heartbeats ( FIG. 1A ) and a magnified portion of the ECG waveform for the first two consecutive heartbeats ( FIG. 1B ); 
         FIG. 2  is a polar plot of a cardiac vector superimposed over a frontal view of a thorax, with the origin of the polar plot located at the atrioventricular (AV) node of a patient&#39;s heart; 
         FIG. 3A  is a polar plot of cardiac vectors obtained using a source separation in accordance with the present invention; 
         FIG. 3B  illustrates polar plots of cardiac vectors obtained from selected portions of an electrocardiogram using source separation in accordance with the present invention; 
         FIG. 4  is a graph of temporal profiles of a cardiac vector useful for diagnosing a cardiac disease in accordance with the present invention; 
         FIGS. 5A through 5D  illustrate cardiac vectors superimposed over a sectional view of the ventricles of a patient&#39;s heart; 
         FIG. 6A  is a block diagram of a method of detecting a change in one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on a source separation in accordance with the present invention; 
         FIG. 6B  is a block diagram of another embodiment of a method of detecting a change in one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on a source separation in accordance with the present invention; 
         FIG. 6C  is a graph illustrating activation sequence vector angles for intrinsic and paced conditions; 
         FIG. 6D  is a graph of asynchrony measurements in accordance with the present invention; 
         FIG. 6E  is a flow chart of a method of titrating CRT in accordance with the present invention; 
         FIG. 7  is a top view of an implantable cardiac device in accordance with the present invention, having at least three electrodes; 
         FIG. 8  is a block diagram of a cardiac activation sequence monitoring and/or tracking process in accordance with the present invention; 
         FIG. 9  is an illustration of an implantable cardiac device including a lead assembly shown implanted in a sectional view of a heart, in accordance with embodiments of the invention; 
         FIG. 10  is a top view of an implantable cardiac device in accordance with the present invention, including an antenna electrode and a lead/header arrangement; 
         FIG. 11  is a diagram illustrating components of a cardiac monitoring and/or stimulation device including an electrode array in accordance with an embodiment of the present invention; 
         FIG. 12  is a block diagram illustrating various components of a cardiac monitoring and/or stimulation device in accordance with an embodiment of the present invention; 
         FIG. 13  is a block diagram of a medical system that may be used to implement system updating, coordinated patient monitoring, diagnosis, and/or therapy in accordance with embodiments of the present invention; 
         FIG. 14  is a block diagram illustrating uses of cardiac activation sequence monitoring and/or tracking in accordance with the present invention; 
         FIG. 15  is a block diagram of a signal separation process in accordance with the present invention; and 
         FIG. 16  is an expanded block diagram of the process illustrated in  FIG. 15 , illustrating an iterative independent component analysis in accordance with the present invention. 
     
    
    
     While the invention is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail below. It is to be understood, however, that the intention is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims. 
     DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS 
     In the following description of the illustrated embodiments, references are made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration, various embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional changes may be made without departing from the scope of the present invention. 
     An implanted device according to the present invention may include one or more of the features, structures, methods, or combinations thereof described hereinbelow. For example, a cardiac monitor or a cardiac stimulator may be implemented to include one or more of the advantageous features and/or processes described below. It is intended that such a monitor, stimulator, or other implanted or partially implanted device need not include all of the features described herein, but may be implemented to include selected features that provide for unique structures and/or functionality. Such a device may be implemented to provide a variety of therapeutic or diagnostic functions. 
     A wide variety of implantable cardiac monitoring and/or stimulation devices may be configured to implement a cardiac activation sequence monitoring and/or tracking methodology used for closed-loop cardiac resynchronization therapy in accordance with the present invention. A non-limiting, representative list of such devices includes cardiac monitors, pacemakers, cardiovertors, defibrillators, resynchronizers, and other cardiac monitoring and therapy delivery devices, including cardiac devices that include or work in coordination with neuro-stimulating devices, drug pumps, or other therapies. These devices may be configured with a variety of electrode arrangements, including transvenous, endocardial, and epicardial electrodes (i.e., intrathoracic electrodes), and/or subcutaneous, non-intrathoracic electrodes, including can, header, and indifferent electrodes, and subcutaneous array or lead electrodes (i.e., non-intrathoracic electrodes). 
     Methods and systems in accordance with the present invention employ cardiac signal separation for closed-loop cardiac resynchronization therapy. Composite cardiac signals are sensed using multiple implantable electrodes. Signal separation is used to produce cardiac activation signal vectors associated with one or more cardiac activation sequences. A change in the signal vector may be detected using subsequent separations. The change may be used to titrate cardiac resynchronization therapy. Information associated with the vectors may be stored and used to track the vectors. 
     Embodiments of the present invention may be implemented in the context of a wide variety of cardiac devices, such as those listed above, and are referred to herein generally as patient-internal medical devices (PIMD) for convenience. A PIMD implemented in accordance with the present invention may incorporate one or more of the electrode types identified above and/or combinations thereof. 
     Cardiac activation sequence monitoring and/or tracking systems of the present invention employ more than two electrodes of varying location, and possibly of varying configuration. In one embodiment, for example, two or more electrodes may conveniently be located on the PIMD header, whereas the can of the PIMD itself may be the third electrode. In another embodiment, one electrode may be located on the PIMD header, another is the can electrode, and a third may be a PIMD antenna used for RF telemetry. 
     Electrocardiogram (ECG) signals originate from electrophysiological signals originating in and propagated through the cardiac tissue, which provide for the cardiac muscle contraction that pumps blood through the body. A sensed ECG signal is effectively a superposition of all the depolarizations occurring within the heart that are associated with cardiac contraction, along with noise components. The propagation of the depolarizations through the heart may be referred to as a depolarization wavefront. The sequence of depolarization wavefront propagation through the chambers of the heart, providing the sequential timing of the heart&#39;s pumping, is designated an activation sequence. 
     A signal separation algorithm may be implemented to separate activation sequence components of ECG signals, and produce one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on the separation. The activation sequence components may be considered as the signal sources that make up the ECG signals, and the signal separation process may be referred to as a source separation process or simply source separation. One illustrative signal source separation methodology useful for producing cardiac signal vectors associated with cardiac activation sequences is designated blind source separation, which will be described in further detail below. 
     In general, the quality of the electrocardiogram or electrogram sensed from one pair of electrodes of a PIMD depends on the orientation of the electrodes with respect to the depolarization wavefront produced by the heart. The signal sensed on an electrode bi-pole is the projection of the ECG vector in the direction of the bi-pole. Cardiac activation sequence monitoring and/or tracking algorithms of the present invention advantageously exploit the strong correlation of signals from a common origin (the heart) across spatially distributed electrodes to detect, monitor, and/or track the activation sequence for closed-loop cardiac resynchronization therapy. 
     Referring to  FIGS. 1A and 1B , an ECG waveform  100  describes the activation sequence of a patient&#39;s heart as recorded, for example, by a bi-polar cardiac sensing electrode. The graph of  FIG. 1A  illustrates an example of the ECG waveform  100  for three heartbeats, denoted as a first heartbeat  110 , a second heartbeat  120 , and a third heartbeat  130 .  FIG. 1B  is a magnified view of the first two heartbeats  110 ,  120  of the ECG waveform identified by bracket  1 B in  FIG. 1A . 
     Referring to the first heartbeat  110 , the portion of the ECG waveform representing depolarization of the atrial muscle fibers is referred to as a P-wave  112 . Depolarization of the ventricular muscle fibers is collectively represented by a Q  114 , R  116 , and S  118  waves of the ECG waveform  100 , typically referred to as the QRS complex, which is a well-known morphologic feature of electrocardiograms. Finally, the portion of the waveform representing repolarization of the ventricular muscle fibers is known as a T wave  119 . Between contractions, the ECG waveform returns to an isopotential level. 
     The sensed ECG waveform  100  illustrated in  FIGS. 1A and 1B  is typical of a far-field ECG signal, effectively a superposition of all the depolarizations occurring within the heart that result in contraction. The ECG waveform  100  may also be obtained indirectly, such as by using a signal separation methodology. Signal separation methodologies, such as blind source separation, are able to separate signals from individual sources that are mixed together into a composite signal. The main principle of signal separation works on the premise that spatially distributed electrodes collect components of a signal from a common origin (e.g., the heart) with the result that these components may be strongly correlated to each other. In addition, these components may also be weakly correlated to components of another origin (e.g., noise). A signal separation algorithm may be implemented to separate these components according to their sources and produce one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on the source separation. 
       FIG. 2  illustrates a convenient reference for describing cardiac signal vectors associated with a depolarization wavefront.  FIG. 2  is a polar plot  200  of a cardiac vector  240  superimposed over a frontal view of a thorax  220 , with the origin of the polar plot located at a patient&#39;s heart  250 , specifically, the atrioventricular (AV) node of the heart  250 . The heart  250  is a four-chambered pump that is largely composed of a special type of striated muscle, called myocardium. Two major pumps operate in the heart, and they are a right ventricle  260 , which pumps blood into pulmonary circulation, and a left ventricle  270 , which pumps blood into the systemic circulation. Each of these pumps is connected to its associated atrium, called a right atrium  265  and a left atrium  275 . 
     The cardiac vector  240  is describable as having an angle, in degrees, about a circle of the polar plot  200 , and having a magnitude, illustrated as a distance from the origin of the tip of the cardiac vector  240 . The polar plot  200  is divided into halves by a horizontal line indicating 0 degrees on the patient&#39;s left, and +/−180 degrees on the patient&#39;s right, and further divided into quadrants by a vertical line indicated by −90 degrees at the patient&#39;s head and +90 degrees on the bottom. The cardiac vector  240  is projectable onto the two-dimensional plane designated by the polar plot  200 . 
     The cardiac vector  240  is a measure of all or a portion of the projection of a heart&#39;s activation sequence onto the polar plot  200 . The heart possesses a specialized conduction system that ensures, under normal conditions, that the overall timing of ventricular and atrial pumping is optimal for producing cardiac output, the amount of blood pumped by the heart per minute. As described earlier, the normal pacemaker of the heart is a self-firing unit located in the right atrium called the sinoatrial node. The electrical depolarization generated by this structure activates contraction of the two atria. The depolarization wavefront then reaches the specialized conduction system using conducting pathways within and between the atria. The depolarization is conducted to the atrioventricular node, and transmitted down a rapid conduction system composed of the right and left bundle branches, to stimulate contraction of the two ventricles. 
     The normal pacemaker and rapid conduction system are influenced by intrinsic automatic activity and by the autonomic nervous system, which modulates heart rate and the speed with which electrical depolarizations are conducted through the specialized conduction system. There are many diseases that interfere with the specialized conduction system of the heart, and many result in abnormally fast, slow, or irregular heart rhythms. 
     The cardiac vector  240  may be, for example, associated with the entire cardiac cycle, and describe the mean magnitude and mean angle of the cardiac cycle. Referring now to  FIG. 3A , a polar plot  300  is illustrated of separate portions of the cardiac cycle that may make up the cardiac vector  240  of  FIG. 2 . As is illustrated in  FIG. 3A , a QRS vector  310  and a P vector  320  are illustrated having approximately 60 degree and 30 degree angles, respectively. The QRS vector  310  may also be referred to as the QRS axis, and changes in the direction of the QRS vector may be referred to as QRS axis deviations. 
     The QRS vector  310  represents the projection of the mean magnitude and angle of the depolarization wavefront during the QRS portion of the cardiac cycle onto the polar plot  300 . The P vector  320  represents the projection of the mean magnitude and angle of the depolarization wavefront during the P portion of the cardiac cycle onto the polar plot  300 . The projection of any portion of the depolarization wavefront may be represented as a vector on the polar plot  300 . 
     Further, any number of cardiac cycles may be combined to provide a statistical sample that may be represented by a vector as a projection onto the polar plot  300 . Likewise, portions of the cardiac cycle over multiple cardiac cycles may also be combined, such as combining a weighted summation of only the P portion of the cardiac cycle over multiple cardiac cycles, for example. 
     Referring now to  FIGS. 1 through 3A , the first, second, and third cardiac cycles  110 ,  120 , and  130  may be analyzed using a window  140  ( FIG. 1 ) applied concurrently to signals sensed by three or more cardiac sense electrodes. The ECG waveform signals  100  from all the sense electrodes, during the window  140 , may be provided to a signal processor. The signal processor may then perform a source separation that provides the cardiac vector  240  ( FIG. 2 ). The cardiac vector  240  then represents the orientation and magnitude of the cardiac vector that is effectively an average over all three cardiac cycles  110 ,  120 , and  130 . 
     Other windows are also useful. For example, a window  150  and a window  160  may provide each full cardiac cycle, such as the cardiac cycle  120  and the cardiac cycle  130  illustrated in  FIG. 1 , to a controller for analysis. The windows  150 ,  160  may be useful for beat-to-beat analysis, where the angle, magnitude, or other useful parameter from the separated cardiac vector  240  is compared between consecutive beats, or trended, for example. 
     Examples of other useful windows include a P-window  152 , a QRS window  154 , and an ST window  155  ( FIG. 1 ) that provide within-beat vector analysis capability, such as by providing the P-vector  320  and the QRS-vector  310  illustrated in  FIG. 3A . Providing a P-window  162  and/or a QRS-window  164 , and/or an ST window  165  to subsequent beats, such as to the consecutive cardiac cycle  130  illustrated in  FIG. 1 , provides for subsequent separations that may provide information for tracking and monitoring changes and/or trends of windowed portions of the cardiac cycle or statistical samples of P, QRS, or T waves over more than 1 beat. 
     Referring now to  FIG. 3B , polar plots of cardiac vectors obtained from selected portions of an electrocardiogram are illustrated. In general, it may be desirable to define one or more detection windows associated with particular segments of a given patient&#39;s cardiac cycle. The detection windows may be associated with cardiac signal features, such as P, QRS, ST, and T wave features, for example. The detection windows may also be associated with other portions of the cardiac cycle that change in character as a result of changes in the pathology of a patient&#39;s heart. Such detection windows may be defined as fixed or triggerable windows. 
     Detection windows may include unit step functions to initiate and terminate the window, or may be tapered or otherwise initiate and terminate using smoothing functions such as Bartlett, Bessel, Butterworth, Hanning, Hamming, Chebyshev, Welch, or other functions and/or filters. The detection windows associated with particular cardiac signal features or segments may have widths sufficient to sense cardiac vectors resulting from normal or expected cardiac activity. Aberrant or unexpected cardiac activity may result in the failure of a given cardiac vector to fall within a range indicative of normal cardiac behavior. Detection of a given cardiac vector beyond a normal range may trigger one or more operations, including increased monitoring or diagnostic operations, therapy delivery, patient or physician alerting, communication of warning and/or device/physiological data to an external system (e.g., advanced patient management system) or other responsive operation. 
     An ECG signal  305  is plotted in  FIG. 3B  as a signal amplitude  350  on the ordinate versus time on the abscissa. One cardiac cycle is illustrated. The P portion of the ECG signal  305  may be defined using a P-window  335  that opens at a time  336  and closes at a time  337 . A source separation performed on the ECG signal  305  within the P-window  335  produces the P vector  310  illustrated on a polar plot  330 . The angle of the P vector  310  indicates the angle of the vector summation of the depolarization wavefront during the time of the P-window  335  for the ECG signal  305 . 
     The ST portion of the ECG signal  305  may be defined using an ST-window  345  that opens at a time  346  and closes at a time  347 . A source separation performed on the ECG signal  305  within the ST-window  345  produces the ST vector  360  illustrated on a polar plot  340 . The angle of the ST vector  360  indicates the angle of the vector summation of the depolarization wavefront during the time of the ST-window  345  for the ECG signal  305 . 
     The P vector  310  and the ST vector  360  may be acquired as baselines, for future comparisons. If baselines for the P vector  310  and the ST vector  360  are already established, the P vector  310  and ST vector  360  may be compared relative to their baselines for monitoring and tracking purposes. As indicated above, detection of P vector  310  or ST vector  360  beyond a predetermined range may trigger one or more responsive operations. 
     Cardiac activation sequence monitoring and tracking, to monitor changes and/or trends as described above, may be useful to determine initial activation sequences, and track acute and chronic changes in the activation sequences. Information from the patient&#39;s activation sequence is valuable for identification, discrimination, and trending of conditions such as conduction anomalies (e.g. AV block, bundle branch block, retrograde conduction) and cardiac arrhythmias (e.g. discriminating between supraventricular tachycardia versus ventricular tachycardia, reentrant supraventricular tachycardia versus atrial fibrillation, or other desirable discrimination.) In addition to baseline establishment, monitoring, and tracking, activation sequence information may also be useful for determining pace capture for autocapture/autothreshold algorithms, adjustment, optimization, or initiation of cardiac resynchronization therapy, and optimization or initiation of anti-arrhythmia therapies, for example. 
       FIG. 4  illustrates another convenient reference for describing cardiac signal vectors associated with a depolarization wavefront.  FIG. 4  is a graph  400  of temporal profiles of a measure of a cardiac vector useful for diagnosing diseases and anomalous conditions in accordance with the present invention. The graph  400  contains a first temporal profile  430  of a cardiac vector, and a second temporal profile  440  of the same cardiac vector after a change has occurred. An abscissa  420  of the graph  400  is time related, and an ordinate  410  of the graph  400  is related to a measure of the cardiac vector. 
     The ordinate  410  may be, for example, the angle of the cardiac vector. A non-limiting, non-exhaustive list of measures of a vector useful for the ordinate  410  includes: angle; magnitude; variance; power spectral density; rate of change of angle; rate of change of magnitude; rate of change of variance; or other measure indicative of a change in the cardiac activation sequence. As an example, consider the angle of the P vector  320  illustrated in  FIG. 3A . In this example, the ordinate  410  would be indicated in degrees, with the first temporal profile  430  varying from around 30 degrees. The abscissa  420  may be time, designated in cardiac cycles, with a measure made of the P vector  320  for every cardiac cycle. The angle of the P vector  320  may be plotted on the graph  400  at any interval of cardiac cycles, thereby displaying variance and trends in the angle of the P vector  320  over many cardiac cycles. 
     After some change occurs, such as a pathological change in the patient&#39;s heart, the second temporal profile  440  may be plotted using cardiac cycles occurring after the change. As is evident in the second temporal profile  440  versus the first temporal profile  430 , the variance of the second temporal profile  440  is significantly larger than the variance of the first temporal profile  430 . Changes such as this may be detected and used to diagnose, verify and/or monitor diseases and/or cardiac conditions in accordance with the present invention. 
       FIGS. 5A through 5D  illustrate cardiac vectors superimposed over a sectional view of the ventricles of a patient&#39;s heart  500 . Referring to  FIG. 5A , the ventricular portion of a patient&#39;s heart is illustrated having a right ventricle  510  and a left ventricle  520  separated by the heart&#39;s septum. The specialized conduction system includes an atrioventricular node  550 , which is used as the origin for cardiac vectors, such as a mean QRS vector  525 . 
     A right bundle branch  530  conducts the depolarization wavefront from the atrioventricular node  550  to the wall of the right ventricle  510 . Illustrated in the wall of the right ventricle  510  are a series of vectors  511 - 517 , indicating the magnitude and angle of a local portion of the depolarization wavefront as it travels along the right ventricle  510 . 
     A left bundle branch  540  conducts the depolarization wavefront from the atrioventricular node  550  to the wall of the left ventricle  520 . Illustrated in the wall of the left ventricle  510  are a series of vectors  501 - 507 , indicating the magnitude and angle of a local portion of the depolarization wavefront as it travels along the left ventricle  520 . 
     The mean QRS vector  525  is the vector summation of the vectors  511 - 517  and the vectors  501 - 507 . The mean QRS vector  525  may be typical of a healthy heart, here illustrated at about 40 degrees angle if using the polar plot of  FIG. 3A . The mean QRS vector  525  varies from patient to patient depending on, for example, patient posture, and normal anatomical variation. 
     Referring now to  FIG. 5B , the wall of the left ventricle  520  is enlarged, or hypertrophied, relative to  FIG. 5A . In  FIG. 5B , a dotted line  560  represents the wall of the left ventricle  520  in  FIG. 5A , before hypertrophy. A series of local vectors  561 - 567  illustrate the larger local contribution to the mean QRS vector  525  from the hypertrophy related vectors  561 - 567  relative to the normal series of left ventricle vectors  501 - 507 . 
       FIG. 5C  illustrates how the mean QRS vector  525  from a normal heart may change to a mean hypertrophied QRS vector  565  after hypertrophy has occurred. For example, a PIMD may be implanted in a patient, and an initial analysis provides a baseline mean QRS vector  525  for the patient, indicative of a normal condition of the left ventricle  520 . After a period of time, the patient&#39;s heart may be subject to hypertrophy. An analysis performed post-hypertrophy may result in finding the mean hypertrophied QRS vector  565 . This change may be used to diagnose, verify and/or monitor hypertrophy of the patient&#39;s left ventricle. 
     Another example of a pathological change that may be diagnosed and/or verified using embodiments of the present invention is a lessening or loss of blood supply to a portion of the heart, such as through a transient ischemia or myocardial infarction. The sectional view in  FIG. 5D  illustrates the left ventricle  520  having an infarcted portion  570  of the ventricular wall. As is evident in the infarcted portion  570 , no depolarization is occurring, so only local depolarization vectors  571 - 574  contribute to the mean cardiac vector from the left ventricle  520 . The infarction results in a change, for example, of the detected mean QRS vector  525  to an infarcted mean QRS vector  580 . Other vectors such as the ST vector may also show the change. This change is evident as the angle of the cardiac vector moves from the second quadrant before infarction, to the third quadrant after infarction. 
     A PIMD that detects a change such as is illustrated in  FIG. 5D  has the potential to alert the patient and/or physician to a loss or lessening of blood supply to a portion of the heart muscle before permanent damage occurs. Early detection may result in greatly reduced morbidity from these kinds of events. 
       FIG. 6A  is a block diagram of a method  600  of detecting a change in one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on a source separation in accordance with the present invention. A baseline is established  610 , providing information that may be monitored or tracked relative to a patient&#39;s electrophysiological signals. The baseline  610  may be established from an initial source separation, that provides initial cardiac signal information as a baseline. Alternately, or additionally, the baseline  610  may be established by a PIMD manufacturer from clinical data, or a patient&#39;s baseline  610  may be established by a clinician before, during, or after a PIMD implant procedure. The baseline  610  may be established as a rolling average of recent patient information from prior source separations, for example. 
     Evaluation criteria is established  620  to provide an index for comparison to the baseline  610 . For example, the evaluation criteria  620  may be any parameter or characteristic determinable or measurable from the patient&#39;s electrophysiology information. A non-exhaustive, non-limiting list of evaluation criteria  620  includes: an angle change of one or more cardiac signal vectors; a magnitude change of one or more cardiac signal vectors; a variance change of one or more cardiac signal vectors; a power spectral density change of the angle of one or more cardiac signal vectors; a power spectral density change of the magnitude of one or more cardiac signal vectors; a trajectory change of one or more cardiac signal vectors; a temporal profile change of one or more cardiac signal vectors; a rate of change of angle of one or more cardiac signal vectors; a rate of change of magnitude of one or more cardiac signal vectors; a rate of change of variance of one or more cardiac signal vectors; a rate of change of temporal profile of one or more cardiac signal vectors; a trend of the angle of one or more cardiac signal vectors; a trend of the magnitude of one or more cardiac signal vectors; a trend of the variance of one or more cardiac signal vectors; and a trend of the temporal profile of one or more cardiac signal vectors. 
     For example, an initial source separation may be performed by a PIMD on a patient post-implant. The separation may produce the baseline  610  of the patient&#39;s average full cardiac cycle, such as the cardiac vector  240  illustrated in  FIG. 2 . The vector  240  may have a characteristic, such as the angle, determined as +45 degrees. The evaluation criteria  620  may be, for example, that the patient&#39;s average full cardiac cycle vector&#39;s angle should be within +40 to +50 degrees. 
     A comparison  630  is performed to determine the latest patient information relative to the baseline  610 . For example, the results of a latest source separation algorithm may provide the latest average full cardiac cycle vector&#39;s angle for the patient. Continuing with the above example, the comparison  630  may check the latest angle of the patient&#39;s average full cardiac cycle vector&#39;s angle against the +40 to +50 degree criteria. 
     A decision  640  selects an outcome based on the comparison  630 . If the criteria is met, for example if the latest angle is within +40 to +50 degrees as outlined above, then a pattern A  650  is considered to be the patient&#39;s latest condition. For example, the pattern A  650  may be defined as an insufficient change to require some sort of action by the PIMD. If the criteria  620  is not met at decision  640 , then a pattern A complement  660  condition is considered to be the patient&#39;s latest condition. The pattern A complement  660  condition may be defined as requiring some sort of action by the PIMD, such as reporting the condition, further evaluating the patient&#39;s cardiac rhythms, preparing a defibrillator for a shock, or other desired action. 
       FIG. 6B  is a block diagram of another embodiment of a method  605  of detecting a change in one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences, when the criteria for baseline shift includes two criteria. It is contemplated that any number of criteria may be used or combined in accordance with the present invention. The use of two criteria with reference to  FIG. 6B  is for purposes of explanation as to how to extend methods of the present invention to multiple criteria, and is not intended as a limiting example. 
     A baseline is established  612 , providing information that may be monitored or tracked from a patient&#39;s electrophysiological signals. The baseline  612  may be established from an initial source separation, that provides initial cardiac signal information as a baseline. Alternately, or additionally, the baseline  612  may be established by a PIMD manufacturer from clinical data, or a patient&#39;s baseline  612  may be established by a clinician before, during, or after a PIMD implant procedure. The baseline  612  may be established as a rolling average of recent patient information from prior source separations, for example. 
     Evaluation criteria are established  622  to provide indices for comparison to the baseline  612 . For example, the evaluation criteria  622  may be any parameters or characteristics determinable or measurable from the patient&#39;s electrophysiology information. A non-exhaustive, non-limiting list of evaluation criteria  622  includes those described previously with respect to  FIG. 6A . It is further contemplated that a single criterion may be compared with respect to multiple baselines, and/or that multiple criteria may each be compared with respect to their own unique baseline established for each particular criterion. 
     Baselines may be pre-defined using, for example, clinical data, and/or baselines may be established using initial source separations. For example, and described in more detail below, a source separation may provide an orthogonal coordinate system, with vectors described using a series of coefficients matched to a series of unit direction vectors. One or more angles may be calculated using trigonometric identities to indicate a vector&#39;s direction relative to other vectors in the coordinate system. Subsequent source separations provide revised sets of coefficients, from which changes in vector direction may be determined using the same trigonometric identities. In an n-dimensional space, (n−1) angles may be resolved and used for comparison and tracking in accordance with the present invention. 
     For example, an initial source separation may be performed by a PIMD on a patient post-implant. The separation may produce the baseline  612  of the patient&#39;s cardiac cycle, such as the QRS-vector  310  and the P-vector  320  illustrated in  FIG. 3A . The QRS-vector  310  may have the angle determined as +45 degrees. The P-vector  320  may have the angle determined as +28 degrees. The evaluation criteria  622  may be, for example, that the patient&#39;s QRS-vector&#39;s angle should be within +40 to +50 degrees and that the patient&#39;s P-vector angle should be within +25 to +30 degrees. 
     A comparison  632  is performed to determine the latest patient information relative to the baseline  612 . For example, the results of a latest source separation algorithm may provide the latest angles of the QRS-vector and P-vector for the patient. Continuing with the above example, the comparison  632  may check the latest angles of the patient&#39;s QRS-vector and P-vector against the +40 to +50 degree and +25 to +30 degree criteria respectively. 
     A first decision  642  selects a first outcome based on the comparison  632 . If the first criteria is met, for example if the latest angle of the QRS-vector is within +40 to +50 degrees as outlined above, then a pattern A  652  is considered to be the patient&#39;s latest condition. For example, the pattern A  652  may be defined as an insufficient change to require some sort of action by the PIMD. If the criteria  622  is not met at decision  642 , then a pattern A complement  662  condition is considered to be the patient&#39;s latest condition. The pattern A complement  662  condition may be defined as requiring some sort of action by the PIMD, such as reporting the condition, further evaluating the patient&#39;s cardiac rhythms, preparing a defibrillator for a shock, or other desired action. 
     A second criteria decision  672  is performed to check for a second outcome based on the second criteria. If the second criteria is met, for example if the latest angle of the P-vector is within +25 to +30 degrees as outlined above, then a pattern B  682  is considered to be the patient&#39;s latest condition. For example, the pattern B  682  may be defined as an insufficient change to require some sort of second action by the PIMD. If the criteria  622  is not met at decision  672 , then a pattern B complement  692  condition is considered to be the patient&#39;s latest condition. The pattern B complement  692  condition may be defined as requiring some sort of second action by the PIMD. 
     Table 1 below provides a non-limiting non-exhaustive list of conditions that may be detected by monitoring and/or tracking cardiac activation sequences in accordance with the present invention. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Conditions associated with QRS Axis Deviations 
               
               
                   
               
             
            
               
                 First Source (Normal −30 to +90 degrees) 
               
            
           
           
               
            
               
                 Left Axis Deviation: ≧−30° 
               
               
                 Left Anterior Fascicular Block (LAFB) axis −45° to −90° 
               
               
                 Some cases of inferior myocardial infarction 
               
               
                 with QR complex 
               
               
                 Inferior Myocardial Infarction + 
               
               
                 LAFB in same patient (QS or QRS complex) 
               
               
                 Some cases of left ventricular hypertrophy 
               
               
                 Some cases of left bundle branch block 
               
               
                 Ostium primum Atrial Septal Defect and other endocardial cushion defects 
               
               
                 Some cases of Wolff-Parkinson-White syndrome syndrome (large negative 
               
               
                 delta wave) 
               
               
                 Right Axis Deviation: ≧+90° 
               
               
                 Left Posterior Fascicular Block (LPFB): 
               
               
                 Many causes of right heart overload and pulmonary hypertension 
               
               
                 High lateral wall Myocardial Infarction 
               
               
                 with QR or QS complex 
               
               
                 Some cases of right bundle branch block 
               
               
                 Some cases of Wolff-Parkinson-White syndrome syndrome 
               
               
                 Children, teenagers, and some young adults 
               
               
                 Bizarre QRS axis: +150° to −90° 
               
               
                 Dextrocardia 
               
               
                 Some cases of complex congenital heart disease (e.g., transposition) 
               
               
                 Some cases of ventricular tachycardia 
               
            
           
           
               
            
               
                 Second Source 
               
            
           
           
               
            
               
                 QRS Axis Deviation 
               
               
                 Left anterior fascicular block (LAFB) 
               
               
                 Right ventricular hypertrophy 
               
               
                 Left bundle branch block Acute Myocardial Infarction: 
               
               
                 Hypertensive heart disease 
               
               
                 Coronary artery disease 
               
               
                 Idiopathic conducting system disease 
               
               
                 Acute Myocardial Infarction - 
               
               
                 inferior left ventricular free wall accessory pathway (Wolff-Parkinson- 
               
               
                 White syndrome) 
               
               
                 Posteroseptal accessory pathway 
               
               
                 left posterior fascicular block 
               
               
                 Chronic Obstructive Pulmonary Disease (uncommon - 10%) 
               
               
                 Other conduction defects: 
               
               
                 left ventricular hypertrophy 
               
               
                 Right bundle branch block 
               
               
                 Elevated diaphragm: R anterior hemiblock 
               
               
                 Pregnancy 
               
               
                 Pacing of R ventricle 
               
               
                 Abdominal mass 
               
               
                 Pulmonary conditions 
               
               
                 Ascites 
               
               
                 Pulmonary hypertension 
               
               
                 Tumor 
               
               
                 Chronic Obstructive Pulmonary Disease 
               
               
                 Conduction defects: Emphysema/bronchitis 
               
               
                 R ventricular (apical) pacing 
               
               
                 Pulmonary emboli/infarcts 
               
               
                 Systemic hypertension, esp. chronic 
               
               
                 Congenital defects 
               
               
                 Valvular lesions 
               
               
                 Rheumatic heart disease 
               
               
                 Pulmonic stenosis 
               
               
                 Aortic regurgitation 
               
               
                 Mitral regurgitation 
               
               
                 Mitral stenosis 
               
               
                 Coarctation of the aorta 
               
               
                 Tricuspid regurgitation 
               
               
                 Hyperkalemia 
               
               
                 Pulmonic stenosis 
               
               
                 Normal variant in obese and in elderly 
               
               
                 Pulmonic regurgitation 
               
               
                   
               
            
           
         
       
     
     Referring now to  FIG. 6C , a graph  402  illustrates vector angles for cardiac activation sequence orientations of intrinsic, ventricle originated, and atrium originated orientations demonstrates the significant departure of ventricle originated activation sequences from atrium originated activation sequences. The graph  402  follows the same axes conventions as  FIGS. 2 and 3A . 
     An intrinsic atrium-originated activation sequence vector  404  and an atrial paced/ventricular sensed activation sequence vector  406  both lie approximately 45 degrees from an atrial sensed/ventricular paced activation sequence vector  408 . The intrinsic atrium-originated activation sequence vector  404  and the atrial paced/ventricular sensed activation sequence vector  406  lie at approximately 80 degrees in the graph  402 . The atrial sensed/ventricular paced activation sequence vector  408  lies at approximately 35 degrees in the graph  402 . This difference is readily detected using activation sequence monitoring and tracking. 
     Monitoring and tracking of cardiac activation sequences provides information about the progression of the depolarization wavefront through the heart, and therefore information about synchronization between the chambers of the heart. Vectors corresponding to the orientation of the depolarization wavefront, such as the vectors  404 ,  406 , and  408  in  FIG. 6C , may be used to determine cardiac asynchrony. Cardiac asynchrony information is useful for titrating CRT therapies, and maximizing hemodynamics. Closed loop control methods of the present invention are useful for updating CRT therapy in a patient, ultimately improving the patient&#39;s quality of life. 
       FIG. 6D  is a graph  412  of asynchrony measurements in accordance with the present invention. The graph  412  has time as its abscissa and signal amplitude as its ordinate. A first trace  414 , a second trace  416 , and a third trace  418  illustrate cardiac signals such as electrocardiogram signals sensed from electrodes of a PIMD. The traces  414 ,  416 , and  418  may be used to measure asynchrony in accordance with the present invention. The first trace  414  is illustrated with a QRS complex width  420  used as a measure of asynchrony. As asynchrony increases, the QRS complex width  420  increases. Measurement of the width of the QRS complex width  420  may be done, for example, using morphological feature analysis. 
     The second trace  416  illustrates a cardiac cycle window  422  used to determine a dominant orientation for the vector associated with the whole cardiac cycle. The portion of the electrocardiogram signal of the trace  416  that lies within the cardiac cycle window  422  may be evaluated using cardiac activation sequence monitoring in accordance with the present invention. Changes in one or more characteristics of one or more vectors associated with the cardiac activation sequence within the cardiac cycle window  422  may be used to determine levels of cardiac asynchrony. For example, a baseline may be established for the angle of the dominant vector orientation for the cardiac signal in the cardiac cycle window  422 . The further subsequent vector&#39;s angles differ from the baseline, the larger the asynchrony. 
     Although the dominant vector (the vector having the largest eigenvalue, or the vector with the largest power spectral density, as will be described in more detail below) is typically the vector that provides the best assessment of cardiac asynchrony, other vectors may also be used. For example, the third trace  424  illustrates a QRS window  424 . The QRS window  424  may provide a vector more closely associated with the depolarization wavefront from the cardiac ventricles. Changes in one or more vectors associated with the QRS window  424  may provide a good measure of cardiac asynchrony. The variation of a characteristic, such as vector angle, over time may be directly correlated to cardiac asynchrony. 
       FIG. 6E  is a flow chart of a method  422  of CRT titration in accordance with the present invention. The method  422  may be initiated at timed intervals, on demand by a clinician, upon detection of a cardiac event, or as otherwise desired. Titration periods may be determined for each patient individually, or may be predetermined using clinical information. If titration  424  is desired, a measure is made of a patient&#39;s ventricular asynchrony  426 , as was previously described. 
     If the asynchrony  426  is measured to be beyond a predetermined value or threshold  428 , a recalculation  432  is performed to determine optimum parameters given current conditions. The optimum parameters are then used to deliver an adjusted CRT therapy  434 . 
     It may be desirable to assess whether the patient responded to the adjusted CRT therapy  434 . If an assessment of the result is desired  436 , then a subsequent measure of the ventricular asynchrony  426  may be performed immediately, or after an opportunity for the adjusted CRT therapy to take effect. If no assessment of the result is desired  436 , then the method returns back to the titration  424  decision. 
     The method  422  provides for a PIMD to automatically titrate CRT therapy when needed. The levels of therapy, as well as the delays, such as atrial-ventricular (AV) delays and interventricular (VV) delays, may be adjusted to maintain improved hemodynamics for the patient. Auto-titration such as is illustrated in  FIG. 6E  may also be combined with auto-capture and auto-threshold techniques to provide patients with adaptive systems that improve efficacy. 
       FIG. 7  is a top view of a PIMD  782  in accordance with the present invention, having at least three electrodes. Although multiple electrodes are illustrated in  FIG. 7  as located on the can, typically the can includes one electrode, and other electrodes are coupled to the can using a lead. The PIMD  782  shown in the embodiment illustrated in  FIG. 7  includes a first electrode  781   a , a second electrode  781   b , and a third electrode  781   c  provided with a can  703 . The PIMD  782  detects and records cardiac activity. The can  703  is illustrated as incorporating a header  789  that may be configured to facilitate removable attachment between one or more leads and the can  703 . The can  703  may include any number of electrodes positioned anywhere in or on the can  703 , such as optional electrodes  781   d ,  781   e ,  781   f , and  781   g . Each electrode pair provides one vector available for the sensing of ECG signals. 
       FIG. 8  is a block diagram of a process  850  useful for extracting vector information for cardiac activation sequence monitoring and tracking in accordance with the present invention. The process  850  starts at block  851 , where multiple concurrent measurements are obtained between multiple respective electrode pairs, chosen from at least three electrodes. Block  852  provides for pre-filtering the collected signals with, for example, a linear-phase filter to suppress broadly incoherent noise, and to generally maximize the signal-to-noise ratio. 
     Block  853  indicates the computation of the cross-correlation matrix, which may be averaged over a relatively short time interval, such as about 1 second. This block enhances the components that are mutually correlated. Block  854  is then provided for computation of the eigenvalues of the cross-correlation matrix. The smaller eigenvalues, normally associated with noise, may then be used at block  855  to eliminate noise, by removing the noise components of the composite signals associated with those eigenvalues. 
     At block  856 , signals may be separated from the composite signals using the eigenvalues. Separated sources may be obtained by taking linear combinations of the recorded signals, as specified in the eigenvectors corresponding to the larger eigenvalues. Optionally, block  857  provides for performing additional separation based on higher order statistics, if the cardiac signal or other signal of interest is not found among the signals separated at block  856 . 
     At block  858 , the cardiac signal may be identified based on the selection criteria, along with its associated vector, among the separated signals. Typically, the cardiac signal is found among the signals associated with the largest eigenvalues. Vector selection and updating systems and methods are further described in commonly assigned co-pending U.S. Pat. No. 7,706,866, which is hereby incorporated herein by reference. 
     For purposes of illustration, and not of limitation, various embodiments of devices that may use cardiac activation sequence monitoring and tracking in accordance with the present invention are described herein in the context of PIMD&#39;s that may be implanted under the skin in the chest region of a patient. A PIMD may, for example, be implanted subcutaneously such that all or selected elements of the device are positioned on the patient&#39;s front, back, side, or other body locations suitable for monitoring cardiac activity and/or delivering cardiac stimulation therapy. It is understood that elements of the PIMD may be located at several different body locations, such as in the chest, abdominal, or subclavian region with electrode elements respectively positioned at different regions near, around, in, or on the heart. 
     The primary housing (e.g., the active or non-active can) of the PIMD, for example, may be configured for positioning outside of the rib cage at an intercostal or subcostal location, within the abdomen, or in the upper chest region (e.g., subclavian location, such as above the third rib). In one implementation, one or more leads incorporating electrodes may be located in direct contact with the heart, great vessel or coronary vasculature, such as via one or more leads implanted by use of conventional transvenous delivery approaches. In another implementation, one or more electrodes may be located on the primary housing and/or at other locations about, but not in direct contact with the heart, great vessel or coronary vasculature. 
     In a further implementation, for example, one or more electrode subsystems or electrode arrays may be used to sense cardiac activity and/or deliver cardiac stimulation energy in a PIMD configuration employing an active can or a configuration employing a non-active can. Electrodes may be situated at anterior and/or posterior locations relative to the heart. Examples of useful electrode locations and features that may be incorporated in various embodiments of the present invention are described in commonly owned, co-pending U.S. Publication No. 2004/0230230 and U.S. Pat. Nos. 7,299,086 and 7,499,750, which are hereby incorporated herein by reference. 
     Certain configurations illustrated herein are generally described as capable of implementing various functions traditionally performed by an implantable cardioverter/defibrillator (ICD), and may operate in numerous cardioversion/defibrillation modes as are known in the art. Examples of ICD circuitry, structures and functionality, aspects of which may be incorporated in a PIMD of a type that may benefit from cardiac activation sequence monitoring and/or tracking are disclosed in commonly owned U.S. Pat. Nos. 5,133,353; 5,179,945; 5,314,459; 5,318,597; 5,620,466; and 5,662,688, which are hereby incorporated herein by reference. 
     In particular configurations, systems and methods may perform functions traditionally performed by pacemakers, such as providing various pacing therapies as are known in the art, in addition to cardioversion/defibrillation therapies. Examples of pacemaker circuitry, structures and functionality, aspects of which may be incorporated in a PIMD of a type that may benefit from cardiac activation sequence monitoring and/or tracking methods and implementations are disclosed in commonly owned U.S. Pat. Nos. 4,562,841; 5,284,136; 5,376,106; 5,036,849; 5,540,727; 5,836,987; 6,044,298; and 6,055,454, which are hereby incorporated herein by reference. It is understood that PIMD configurations may provide for non-physiologic pacing support in addition to, or to the exclusion of, bradycardia and/or anti-tachycardia pacing therapies. 
     A PIMD useful for extracting vector information for cardiac activation sequence monitoring and tracking in accordance with the present invention may implement diagnostic and/or monitoring functions as well as provide cardiac stimulation therapy. Examples of cardiac monitoring circuitry, structures and functionality, aspects of which may be incorporated in a PIMD of a type that may benefit from cardiac activation sequence monitoring and/or tracking methods and implementations are disclosed in commonly owned U.S. Pat. Nos. 5,313,953; 5,388,578; and 5,411,031, which are hereby incorporated herein by reference. 
     Various embodiments described herein may be used in connection with congestive heart failure (CHF) monitoring, diagnosis, and/or therapy. A PIMD of the present invention may incorporate CHF features involving dual-chamber or bi-ventricular pacing therapy, cardiac resynchronization therapy, cardiac function optimization, or other CHF related methodologies. For example, any PIMD of the present invention may incorporate features of one or more of the following references: commonly owned U.S. Pat. Nos. 7,260,432; 6,411,848; 6,285,907; 4,928,688; 6,459,929; 5,334,222; 6,026,320; 6,371,922; 6,597,951; 6,424,865; and 6,542,775, each of which is hereby incorporated herein by reference. 
     A PIMD may be used to implement various diagnostic functions, which may involve performing rate-based, pattern and rate-based, and/or morphological tachyarrhythmia discrimination analyses. Subcutaneous, cutaneous, and/or external sensors may be employed to acquire physiologic and non-physiologic information for purposes of enhancing tachyarrhythmia detection and termination. It is understood that configurations, features, and combination of features described in the present disclosure may be implemented in a wide range of implantable medical devices, and that such embodiments and features are not limited to the particular devices described herein. 
     Referring now to  FIG. 9 , the implantable device illustrated in  FIG. 9  is an embodiment of a PIMD that may benefit from cardiac sequence monitoring and tracking in accordance with the present invention. In this example, the implantable device includes a cardiac rhythm management device (CRM)  900  including an implantable pulse generator  905  electrically and physically coupled to an intracardiac lead system  910 . 
     Portions of the intracardiac lead system  910  are inserted into the patient&#39;s heart  990 . The intracardiac lead system  910  includes one or more electrodes configured to sense electrical cardiac activity of the heart, deliver electrical stimulation to the heart, sense the patient&#39;s transthoracic impedance, and/or sense other physiological parameters, e.g., cardiac chamber pressure or temperature. Portions of the housing  901  of the pulse generator  905  may optionally serve as a can electrode. 
     Communications circuitry is disposed within the housing  901  for facilitating communication between the pulse generator  905  and an external communication device, such as a portable or bed-side communication station, patient-carried/worn communication station, or external programmer, for example. The communications circuitry may also facilitate unidirectional or bidirectional communication with one or more implanted, external, cutaneous, or subcutaneous physiologic or non-physiologic sensors, patient-input devices and/or information systems. 
     The pulse generator  905  may optionally incorporate a motion detector  920  that may be used to sense patient activity as well as various respiratory and cardiac related conditions. For example, the motion detector  920  may be optionally configured to sense snoring, activity level, and/or chest wall movements associated with respiratory effort, for example. The motion detector  920  may be implemented as an accelerometer positioned in or on the housing  901  of the pulse generator  905 . If the motion sensor is implemented as an accelerometer, the motion sensor may also provide respiratory, e.g. rales, coughing, and cardiac, e.g. S1-S4 heart sounds, murmurs, and other acoustic information. 
     The lead system  910  and pulse generator  905  of the CRM  900  may incorporate one or more transthoracic impedance sensors that may be used to acquire the patient&#39;s respiratory waveform, or other respiratory-related information. The transthoracic impedance sensor may include, for example, one or more intracardiac electrodes  941 ,  942 ,  951 - 955 ,  963  positioned in one or more chambers of the heart  990 . The intracardiac electrodes  941 ,  942 ,  951 - 955 ,  963  may be coupled to impedance drive/sense circuitry  930  positioned within the housing of the pulse generator  905 . 
     In one implementation, impedance drive/sense circuitry  930  generates a current that flows through the tissue between an impedance drive electrode  951  and a can electrode on the housing  901  of the pulse generator  905 . The voltage at an impedance sense electrode  952  relative to the can electrode changes as the patient&#39;s transthoracic impedance changes. The voltage signal developed between the impedance sense electrode  952  and the can electrode is detected by the impedance sense circuitry  930 . Other locations and/or combinations of impedance sense and drive electrodes are also possible. 
     The lead system  910  may include one or more cardiac pace/sense electrodes  951 - 955  positioned in, on, or about one or more heart chambers for sensing electrical signals from the patient&#39;s heart  990  and/or delivering pacing pulses to the heart  990 . The intracardiac sense/pace electrodes  951 - 955 , such as those illustrated in  FIG. 9 , may be used to sense and/or pace one or more chambers of the heart, including the left ventricle, the right ventricle, the left atrium and/or the right atrium. The lead system  910  may include one or more defibrillation electrodes  941 ,  942  for delivering defibrillation/cardioversion shocks to the heart. 
     The pulse generator  905  may include circuitry for detecting cardiac arrhythmias and/or for controlling pacing or defibrillation therapy in the form of electrical stimulation pulses or shocks delivered to the heart through the lead system  910 . The pulse generator  905  may also incorporate circuitry, structures and functionality of the implantable medical devices disclosed in commonly owned U.S. Pat. Nos. 5,203,348; 5,230,337; 5,360,442; 5,366,496; 5,397,342; 5,391,200; 5,545,202; 5,603,732; and 5,916,243; 6,360,127; 6,597,951; and US Patent Publication No. 2002/0143264, which are hereby incorporated herein by reference. 
       FIG. 10  is a top view of a PIMD  1082  in accordance with the present invention, having at least three electrodes. One electrode is illustrated as an antenna  1005  of the PIMD that may also be used for radio-frequency (RF) communications. The PIMD  1082  shown in the embodiment illustrated in  FIG. 10  includes a first electrode  1098  and a second electrode  1099  coupled to a can  1003  through a header  1089 , via an electrode module  1096 . The first electrode  1098  and second electrode  1099  may be located on a lead  1083  (single or multiple lead, or electrode array), or may be located directly in or on the electrode module  1096 . 
     The PIMD  1082  detects and records cardiac activity. The can  1003  is illustrated as incorporating the header  1089 . The header  1089  may be configured to facilitate removable attachment between an electrode module  1096  and the can  1003 , as is shown in the embodiment depicted in  FIG. 10 . The header  1089  includes a female coupler  1092  configured to accept a male coupler  1093  from the electrode module  1096 . The male coupler  1093  is shown having two electrode contacts  1094 ,  1095  for coupling one or more electrodes  1098 ,  1099  through the electrode module  1096  to the can  1003 . An electrode  1081   h  and an electrode  1081   k  are illustrated on the header  1089  of the can  1003  and may also be coupled through the electrode module  1096  to the can  1003 . The can  1003  may alternatively, or in addition to the header electrodes  1081   h ,  1081   k  and/or first and second electrodes  1098 ,  1099 , include one or more can electrodes  1081   a ,  1081   b ,  1081   c.    
     Recording and monitoring systems and methods that may benefit from cardiac activation sequence monitoring and tracking in accordance with the present invention are further described in commonly assigned co-pending U.S. patent application Ser. No. 10/785,431 filed Feb. 24, 2004 entitled “Reconfigurable Implantable Cardiac Monitoring And Therapy Delivery Device” hereby incorporated herein by reference. 
     Electrodes may also be provided on the back of the can  1003 , typically the side facing externally relative to the patient after implantation. For example, electrodes  1081   m ,  1081   p , and  1081   r  are illustrated as positioned in or on the back of the can  1003 . Providing electrodes on both front and back surfaces of the can  1003  provides for a three-dimensional spatial distribution of the electrodes, which may provide additional discrimination capabilities for cardiac activation sequence monitoring and tracking in accordance with the present invention. Further description of three-dimensional configurations are described in U.S. Pat. No. 7,299,086, previously incorporated by reference. 
     In this and other configurations, the header  1089  incorporates interface features (e.g., electrical connectors, ports, engagement features, and the like) that facilitate electrical connectivity with one or more lead and/or sensor systems, lead and/or sensor modules, and electrodes. The header  1089  may also incorporate one or more electrodes in addition to, or instead of, the electrodes provided by the lead  1083 , such as electrodes  1081   h  and  1081   k , to provide more available vectors to the PIMD. The interface features of the header  1089  may be protected from body fluids using known techniques. 
     The PIMD  1082  may further include one or more sensors in or on the can  1003 , header  1089 , electrode module  1096 , or lead(s) that couple to the header  1089  or electrode module  1096 . Useful sensors may include electrophysiologic and non-electrophysiologic sensors, such as an acoustic sensor, an impedance sensor, a blood sensor, such as an oxygen saturation sensor (oximeter or plethysmographic sensor), a blood pressure sensor, minute ventilation sensor, or other sensor described or incorporated herein. 
     In one configuration, as is illustrated in  FIG. 11 , electrode subsystems of a PIMD system are arranged about a patient&#39;s heart  1110 . The PIMD system includes a first electrode subsystem, including a can electrode  1102 , and a second electrode subsystem  1104  that includes at least two electrodes or at least one multi-element electrode. The second electrode subsystem  1104  may include a number of electrodes used for sensing and/or electrical stimulation and is connected to pulse generator  905  via lead  1106 . 
     In various configurations, the second electrode subsystem  1104  may include a combination of electrodes. The combination of electrodes of the second electrode subsystem  1104  may include coil electrodes, tip electrodes, ring electrodes, multi-element coils, spiral coils, spiral coils mounted on non-conductive backing, screen patch electrodes, and other electrode configurations as will be described below. A suitable non-conductive backing material is silicone rubber, for example. 
     The can electrode  1102  is positioned on the housing  1101  that encloses the PIMD electronics. In one embodiment, the can electrode  1102  includes the entirety of the external surface of housing  1101 . In other embodiments, various portions of the housing  1101  may be electrically isolated from the can electrode  1102  or from tissue. For example, the active area of the can electrode  1102  may include all or a portion of either the anterior or posterior surface of the housing  1101  to direct current flow in a manner advantageous for cardiac sensing and/or stimulation. 
     Portions of the housing may be electrically isolated from tissue to optimally direct current flow. For example, portions of the housing  1101  may be covered with a non-conductive, or otherwise electrically resistive, material to direct current flow. Suitable non-conductive material coatings include those formed from silicone rubber, polyurethane, or parylene, for example. 
       FIG. 12  is a block diagram depicting various componentry of different arrangements of a PIMD in accordance with embodiments of the present invention. The components, functionality, and configurations depicted in  FIG. 12  are intended to provide an understanding of various features and combinations of features that may be incorporated in a PIMD. It is understood that a wide variety of device configurations are contemplated, ranging from relatively sophisticated to relatively simple designs. As such, particular PIMD configurations may include some componentry illustrated in  FIG. 12 , while excluding other componentry illustrated in  FIG. 12 . 
     Illustrated in  FIG. 12  is a processor-based control system  1205  which includes a micro-processor  1206  coupled to appropriate memory (volatile and/or non-volatile)  1209 , it being understood that any logic-based control architecture may be used. The control system  1205  is coupled to circuitry and components to sense, detect, and analyze electrical signals produced by the heart and deliver electrical stimulation energy to the heart under predetermined conditions to treat cardiac arrhythmias and/or other cardiac conditions. The control system  1205  and associated components also provide pacing therapy to the heart. The electrical energy delivered by the PIMD may be in the form of low energy pacing pulses or high-energy pulses for cardioversion or defibrillation. 
     Cardiac signals are sensed using the electrode(s)  1214  and the can or indifferent electrode  1207  provided on the PIMD housing. Cardiac signals may also be sensed using only the electrode(s)  1214 , such as in a non-active can configuration. As such, unipolar, bipolar, or combined unipolar/bipolar electrode configurations as well as multi-element electrodes and combinations of noise canceling and standard electrodes may be employed. The sensed cardiac signals are received by sensing circuitry  1204 , which includes sense amplification circuitry and may also include filtering circuitry and an analog-to-digital (A/D) converter. The sensed cardiac signals processed by the sensing circuitry  1204  may be received by noise reduction circuitry  1203 , which may further reduce noise before signals are sent to the detection circuitry  1202 . 
     Noise reduction circuitry  1203  may also be incorporated after sensing circuitry  1204  in cases where high power or computationally intensive noise reduction algorithms are required. The noise reduction circuitry  1203 , by way of amplifiers used to perform operations with the electrode signals, may also perform the function of the sensing circuitry  1204 . Combining the functions of sensing circuitry  1204  and noise reduction circuitry  1203  may be useful to minimize the necessary componentry and lower the power requirements of the system. 
     In the illustrative configuration shown in  FIG. 12 , the detection circuitry  1202  is coupled to, or otherwise incorporates, noise reduction circuitry  1203 . The noise reduction circuitry  1203  operates to improve the SNR of sensed cardiac signals by removing noise content of the sensed cardiac signals introduced from various sources. Typical types of cardiac signal noise includes electrical noise and noise produced from skeletal muscles, for example. A number of methodologies for improving the SNR of sensed cardiac signals in the presence of skeletal muscular induced noise, including signal separation techniques incorporating combinations of electrodes and multi-element electrodes, are described hereinbelow. 
     Detection circuitry  1202  may include a signal processor that coordinates analysis of the sensed cardiac signals and/or other sensor inputs to detect cardiac arrhythmias, such as, in particular, tachyarrhythmia. Rate based and/or morphological discrimination algorithms may be implemented by the signal processor of the detection circuitry  1202  to detect and verify the presence and severity of an arrhythmic episode. Examples of arrhythmia detection and discrimination circuitry, structures, and techniques, aspects of which may be implemented by a PIMD of a type that may benefit from cardiac activation sequence monitoring and/or tracking methods and implementations are disclosed in commonly owned U.S. Pat. Nos. 5,301,677, 6,438,410, and 6,708,058, which are hereby incorporated herein by reference. Arrhythmia detection methodologies particularly well suited for implementation in cardiac monitoring and/or stimulation systems are described hereinbelow. 
     The detection circuitry  1202  communicates cardiac signal information to the control system  1205 . Memory circuitry  1209  of the control system  1205  contains parameters for operating in various monitoring, defibrillation, and, if applicable, pacing modes, and stores data indicative of cardiac signals received by the detection circuitry  1202 . The memory circuitry  1209  may also be configured to store historical ECG and therapy data, which may be used for various purposes and transmitted to an external receiving device as needed or desired. 
     In certain configurations, the PIMD may include diagnostics circuitry  1210 . The diagnostics circuitry  1210  typically receives input signals from the detection circuitry  1202  and the sensing circuitry  1204 . The diagnostics circuitry  1210  provides diagnostics data to the control system  1205 , it being understood that the control system  1205  may incorporate all or part of the diagnostics circuitry  1210  or its functionality. The control system  1205  may store and use information provided by the diagnostics circuitry  1210  for a variety of diagnostics purposes. This diagnostic information may be stored, for example, subsequent to a triggering event or at predetermined intervals, and may include system diagnostics, such as power source status, therapy delivery history, and/or patient diagnostics. The diagnostic information may take the form of electrical signals or other sensor data acquired immediately prior to therapy delivery. 
     According to a configuration that provides cardioversion and defibrillation therapies, the control system  1205  processes cardiac signal data received from the detection circuitry  1202  and initiates appropriate tachyarrhythmia therapies to terminate cardiac arrhythmic episodes and return the heart to normal sinus rhythm. The control system  1205  is coupled to shock therapy circuitry  1216 . The shock therapy circuitry  1216  is coupled to the electrode(s)  1214  and the can or indifferent electrode  1207  of the PIMD housing. 
     Upon command, the shock therapy circuitry  1216  delivers cardioversion and defibrillation stimulation energy to the heart in accordance with a selected cardioversion or defibrillation therapy. In a less sophisticated configuration, the shock therapy circuitry  1216  is controlled to deliver defibrillation therapies, in contrast to a configuration that provides for delivery of both cardioversion and defibrillation therapies. Examples of PIMD high energy delivery circuitry, structures and functionality, aspects of which may be incorporated in a PIMD of a type that may benefit from aspects of the present invention are disclosed in commonly owned U.S. Pat. Nos. 5,372,606; 5,411,525; 5,468,254; and 5,634,938, which are hereby incorporated herein by reference. 
     Arrhythmic episodes may also be detected and verified by morphology-based analysis of sensed cardiac signals as is known in the art. Tiered or parallel arrhythmia discrimination algorithms may also be implemented using both rate-based and morphologic-based approaches. Further, a rate and pattern-based arrhythmia detection and discrimination approach may be employed to detect and/or verify arrhythmic episodes, such as the approach disclosed in U.S. Pat. Nos. 6,487,443; 6,259,947; 6,141,581; 5,855,593; and 5,545,186, which are hereby incorporated herein by reference. 
     In accordance with another configuration, a PIMD may incorporate a cardiac pacing capability in addition to, or to the exclusion of, cardioversion and/or defibrillation capabilities. As is shown in  FIG. 12 , the PIMD includes pacing therapy circuitry  1230  that is coupled to the control system  1205  and the electrode(s)  1214  and can/indifferent electrodes  1207 . Upon command, the pacing therapy circuitry  1230  delivers pacing pulses to the heart in accordance with a selected pacing therapy. 
     Control signals, developed in accordance with a pacing regimen by pacemaker circuitry within the control system  1205 , are initiated and transmitted to the pacing therapy circuitry  1230  where pacing pulses are generated. A pacing regimen, such as those discussed and incorporated herein, may be modified by the control system  1205 . In one particular application, a sense vector optimization approach of the present invention may be implemented to enhance capture detection and/or capture threshold determinations, such as by selecting an optimal vector for sensing an evoked response resulting from application of a capture pacing stimulus. 
     The PIMD shown in  FIG. 12  may be configured to receive signals from one or more physiologic and/or non-physiologic sensors. Depending on the type of sensor employed, signals generated by the sensors may be communicated to transducer circuitry coupled directly to the detection circuitry  1202  or indirectly via the sensing circuitry  1204 . It is noted that certain sensors may transmit sense data to the control system  1205  without processing by the detection circuitry  1202 . 
     Communications circuitry  1218  is coupled to the microprocessor  1206  of the control system  1205 . The communications circuitry  1218  allows the PIMD to communicate with one or more receiving devices or systems situated external to the PIMD. By way of example, the PIMD may communicate with a patient-worn, portable or bedside communication system via the communications circuitry  1218 . In one configuration, one or more physiologic or non-physiologic sensors (subcutaneous, cutaneous, or external of patient) may be equipped with a short-range wireless communication interface, such as an interface conforming to a known communications standard, such as Bluetooth or IEEE 802 standards. Data acquired by such sensors may be communicated to the PIMD via the communications circuitry  1218 . It is noted that physiologic or non-physiologic sensors equipped with wireless transmitters or transceivers may communicate with a receiving system external of the patient. 
     The communications circuitry  1218  allows the PIMD to communicate with an external programmer. In one configuration, the communications circuitry  1218  and the programmer unit (not shown) use a wire loop antenna and a radio frequency telemetric link, as is known in the art, to receive and transmit signals and data between the programmer unit and communications circuitry  1218 . In this manner, programming commands and data are transferred between the PIMD and the programmer unit during and after implant. Using a programmer, a physician is able to set or modify various parameters used by the PIMD. For example, a physician may set or modify parameters affecting monitoring, detection, pacing, and defibrillation functions of the PIMD, including pacing and cardioversion/defibrillation therapy modes. 
     Typically, the PIMD is encased and hermetically sealed in a housing suitable for implanting in a human body as is known in the art. Power to the PIMD is supplied by an electrochemical power source  1220  housed within the PIMD. In one configuration, the power source  1220  includes a rechargeable battery. According to this configuration, charging circuitry is coupled to the power source  1220  to facilitate repeated non-invasive charging of the power source  1220 . The communications circuitry  1218 , or separate receiver circuitry, is configured to receive RF energy transmitted by an external RF energy transmitter. The PIMD may, in addition to a rechargeable power source, include a non-rechargeable battery. It is understood that a rechargeable power source need not be used, in which case a long-life non-rechargeable battery is employed. 
     The detection circuitry  1202 , which is coupled to a microprocessor  1206 , may be configured to incorporate, or communicate with, specialized circuitry for processing sensed cardiac signals in manners particularly useful in a cardiac sensing and/or stimulation device. As is shown by way of example in  FIG. 12 , the detection circuitry  1202  may receive information from multiple physiologic and non-physiologic sensors. 
     The detection circuitry  1202  may also receive information from one or more sensors that monitor skeletal muscle activity. In addition to cardiac activity signals, electrodes readily detect skeletal muscle signals. Such skeletal muscle signals may be used to determine the activity level of the patient. In the context of cardiac signal detection, such skeletal muscle signals are considered artifacts of the cardiac activity signal, which may be viewed as noise. 
     The components, functionality, and structural configurations depicted herein are intended to provide an understanding of various features and combination of features that may be incorporated in a PIMD. It is understood that a wide variety of PIMDs and other implantable cardiac monitoring and/or stimulation device configurations are contemplated, ranging from relatively sophisticated to relatively simple designs. As such, particular PIMD or cardiac monitoring and/or stimulation device configurations may include particular features as described herein, while other such device configurations may exclude particular features described herein. 
     The PIMD may detect a variety of physiological signals that may be used in connection with various diagnostic, therapeutic or monitoring implementations. For example, the PIMD may include sensors or circuitry for detecting respiratory system signals, cardiac system signals, and signals related to patient activity. In one embodiment, the PIMD senses intrathoracic impedance, from which various respiratory parameters may be derived, including, for example, respiratory tidal volume and minute ventilation. Sensors and associated circuitry may be incorporated in connection with a PIMD for detecting one or more body movement or body posture or position related signals. For example, accelerometers and GPS devices may be employed to detect patient activity, patient location, body orientation, or torso position. 
     Referring now to  FIG. 13 , a PIMD of the present invention may be used within the structure of an advanced patient management (APM) system  1300 . The APM system  1300  allows physicians and/or other clinicians to remotely and automatically monitor cardiac and respiratory functions, as well as other patient conditions. The APM system  1300  may also be used to provide information to the PIMD for incorporation into templates, such as medication information or other patient information useful in accordance with the present invention. The APM system  1300  may also be used to select portions of cardiac waveforms for which templates are desired. The APM system  1300  may also be used to select or eliminate therapies associate with templates. In one example, a PIMD implemented as a cardiac pacemaker, defibrillator, or resynchronization device may be equipped with various telecommunications and information technologies that enable real-time data collection, diagnosis, and treatment of the patient. 
     Various PIMD embodiments described herein may be used in connection with advanced patient management. Methods, structures, and/or techniques described herein, which may be adapted to provide for remote patient/device monitoring, diagnosis, therapy, or other APM related methodologies, may incorporate features of one or more of the following references: U.S. Pat. Nos. 6,221,011; 6,270,457; 6,277,072; 6,280,380; 6,312,378; 6,336,903; 6,358,203; 6,368,284; 6,398,728; and 6,440,066, which are hereby incorporated herein by reference. 
     As is illustrated in  FIG. 13 , the medical system  1300  may be used to implement template generation, template updating, template initialization, template selection, patient measuring, patient monitoring, patient diagnosis, patient therapy, therapy selection, and/or therapy elimination in accordance with embodiments of the invention. The medical system  1300  may include, for example, one or more patient-internal medical devices  1310 , such as a PIMD, and one or more patient-external medical devices  1320 , such as a monitor or signal display device. Each of the patient-internal  1310  and patient-external  1320  medical devices may include one or more of a patient monitoring unit  1312 ,  1322 , a diagnostics unit  1314 ,  1324 , and/or a therapy unit  1316 ,  1326 . 
     The patient-external medical device  1320  performs monitoring, and/or diagnosis and/or therapy functions external to the patient (i.e., not invasively implanted within the patient&#39;s body). The patient-external medical device  1320  may be positioned on the patient, near the patient, or in any location external to the patient. 
     The patient-internal and patient-external medical devices  1310 ,  1320  may be coupled to one or more sensors  1341 ,  1342 ,  1345 ,  1346 , patient input/trigger devices  1343 ,  1347  and/or other information acquisition devices  1344 ,  1348 . The sensors  1341 ,  1342 ,  1345 ,  1346 , patient input/trigger devices  1343 ,  1347 , and/or other information acquisition devices  1344 ,  1348  may be employed to detect conditions relevant to the monitoring, diagnostic, and/or therapeutic functions of the patient-internal and patient-external medical devices  1310 ,  1320 . 
     The medical devices  1310 ,  1320  may each be coupled to one or more patient-internal sensors  1341 ,  1345  that are fully or partially implantable within the patient. The medical devices  1310 ,  1320  may also be coupled to patient-external sensors positioned on, near, or in a remote location with respect to the patient. The patient-internal and patient-external sensors are used to sense conditions, such as physiological or environmental conditions, that affect the patient. 
     The patient-internal sensors  1341  may be coupled to the patient-internal medical device  1310  through one or more internal leads  1353 . Still referring to  FIG. 13 , one or more patient-internal sensors  1341  may be equipped with transceiver circuitry to support wireless communications between the one or more patient-internal sensors  1341  and the patient-internal medical device  1310  and/or the patient-external medical device  1320 . The patient-internal sensors  1345  may be coupled to the patient-external medical device  1320  through a wireless connection  1359 , and/or using communications between the patient-internal medical device  1310  and the patient-external medical device  1320 , or may be coupled using a wire or other communications channel. 
     The patient-external sensors  1342  may be coupled to the patient-internal medical device  1310  through one or more internal leads  1355 . Patient-external sensors  1342  may communicate with the patient-internal medical device  1310  wirelessly. Patient-external sensors  1342  may be coupled to the patient-external medical device  1320  through one or more leads  1357  or through a wireless link. 
     In an embodiment of the present invention, the patient-external medical device  1320  includes a visual display configured to concurrently display non-electrophysiological signals and intracardiac electrogram signals. For example, the display may present the information visually. The patient-external medical device  1320  may also, or alternately, provide signals to other components of the medical system  1300  for presentation to a clinician, whether local to the patient or remote to the patient. 
     Referring still to  FIG. 13 , the medical devices  1310 ,  1320  may be connected to one or more information acquisition devices  1344 ,  1348 , such as a database that stores information useful in connection with the monitoring, diagnostic, or therapy functions of the medical devices  1310 ,  1320 . For example, one or more of the medical devices  1310 ,  1320  may be coupled through a network to a patient information server  1330 . 
     The input/trigger devices  1343 ,  1347  are used to allow the physician, clinician, and/or patient to manually trigger and/or transfer information to the medical devices  1310 ,  1320  and/or from the APM system  1340  and/or patient-external medical device  1320  back to the patient-internal device  1310 . The input/trigger devices  1343 ,  1347  may be particularly useful for inputting information concerning patient perceptions, such as a perceived cardiac event, how well the patient feels, and other information not automatically sensed or detected by the medical devices  1310 ,  1320 . For example, the patient may trigger the input/trigger device  1343  upon perceiving a cardiac event. The trigger may then initiate the recording of cardiac signals and/or other sensor signals in the patient-internal device  1310 . Later, a clinician may trigger the input/trigger device  1347 , initiating the transfer of the recorded cardiac and/or other signals from the patient-internal device  1310  to the patient-external device  1320  for display and diagnosis. 
     In one embodiment, the patient-internal medical device  1310  and the patient-external medical device  1320  may communicate through a wireless link between the medical devices  1310 ,  1320 . For example, the patient-internal and patient-external devices  1310 ,  1320  may be coupled through a short-range radio link, such as Bluetooth, IEEE 802.11, and/or a proprietary wireless protocol. The communications link may facilitate uni-directional or bi-directional communication between the patient-internal  1310  and patient-external  1320  medical devices. Data and/or control signals may be transmitted between the patient-internal  1310  and patient-external  1320  medical devices to coordinate the functions of the medical devices  1310 ,  1320 . 
     In another embodiment, patient data may be downloaded from one or more of the medical devices periodically or on command, and stored at the patient information server  1330 . The physician and/or the patient may communicate with the medical devices and the patient information server  1330 , for example, to acquire patient data or to initiate, terminate or modify recording and/or therapy. 
     The data stored on the patient information server  1330  may be accessible by the patient and the patient&#39;s physician through one or more terminals  1350 , e.g., remote computers located in the patient&#39;s home or the physician&#39;s office. The patient information server  1330  may be used to communicate to one or more of the patient-internal and patient-external medical devices  1310 ,  1320  to provide remote control of the monitoring, diagnosis, and/or therapy functions of the medical devices  1310 ,  1320 . 
     In one embodiment, the patient&#39;s physician may access patient data transmitted from the medical devices  1310 ,  1320  to the patient information server  1330 . After evaluation of the patient data, the patient&#39;s physician may communicate with one or more of the patient-internal or patient-external devices  1310 ,  1320  through an APM system  1340  to initiate, terminate, or modify the monitoring, diagnostic, and/or therapy functions of the patient-internal and/or patient-external medical systems  1310 ,  1320 . 
     In another embodiment, the patient-internal and patient-external medical devices  1310 ,  1320  may not communicate directly, but may communicate indirectly through the APM system  1340 . In this embodiment, the APM system  1340  may operate as an intermediary between two or more of the medical devices  1310 ,  1320 . For example, data and/or control information may be transferred from one of the medical devices  1310 ,  1320  to the APM system  1340 . The APM system  1340  may transfer the data and/or control information to another of the medical devices  1310 ,  1320 . 
     In one embodiment, the APM system  1340  may communicate directly with the patient-internal and/or patient-external medical devices  1310 ,  1320 . In another embodiment, the APM system  1340  may communicate with the patient-internal and/or patient-external medical devices  1310 ,  1320  through medical device programmers  1360 ,  1370  respectively associated with each medical device  1310 ,  1320 . As was stated previously, the patient-internal medical device  1310  may take the form of an implantable PIMD. 
     In accordance with one approach of the present invention, a PIMD may be implemented to separate cardiac signals for selection and monitoring of vectors in a robust manner using a blind source separation technique. It is understood that all or certain aspects of the BSS technique described below may be implemented in a device or system (implantable or non-implantable) other than a PIMD, and that the description of BSS techniques implemented in a PIMD is provided for purposes of illustration, and not of limitation. For example, algorithms that implement a BSS technique as described below may be implemented for use by an implanted processor or a non-implanted processor, such as a processor of a programmer or computer of a patient-external device communicatively coupled to the PIMD. 
     Referring now to  FIGS. 14 through 16 , cardiac monitoring and/or stimulation devices and methods employing cardiac signal separation are described in accordance with the present invention. The PIMD may be implemented to separate signal components according to their sources and produce one or more cardiac signal vectors associated with all or a portion of one or more cardiac activation sequences based on the source separation. To achieve this, the methods and algorithms illustrated in  FIGS. 14 through 16  may be implemented. 
       FIG. 14  illustrates a portion of a cardiac activation sequence monitoring and/or tracking system  1425  in accordance with the present invention. A process  1414  is performed, providing a selected vector  1419  along with vector information including, for example, magnitude, angle, rates of change, trend information, and other statistics. The selected vector  1419  (and associated signal and other vector information) is available for a variety of uses  1420 , such as, for example, arrhythmia discrimination, therapy titration, posture detection/monitoring, ischemia detection/monitoring, capture verification, disease diagnosis and/or progress information, or other use. In accordance with the present invention, the process may be used, and repeated, to monitor cardiac activation sequences, track changes in the progression of patient pathology, and to update sense vectors useful for cardiac sensing and/or stimulation, for example. 
       FIG. 15  illustrates an embodiment of a signal source separation/update process  1500  useful for cardiac activation sequence monitoring and/or tracking in accordance with the present invention. A set of composite signals, including at least two and up to n signals, are selected for separation, where n is an integer. Each electrode provides a composite signal associated with an unknown number of sources. Pre-processing and/or pre-filtering  1612  may be performed on each of the composite signals. It may be advantageous to filter each composite signal using the same filtering function. Source separation  1614  is performed, providing at least one separated signal. If a treatment is desired, an appropriate treatment or therapy  1618  is performed. If continued source separation is desired, the process returns to perform such source separation  1614  and may iteratively separate  1616  more signals until a desired signal is found, or all signals are separated. 
     The separated signal or signals may then be used  1620  for some specified purpose, such as, for example, to confirm a normal sinus rhythm, determine a cardiac condition, define a noise signal, monitor cardiac activation sequence, determine patient posture, diagnose or monitor a disease state, or other desired use. Electrode arrays and/or the use of multiple electrodes provide for many possible vectors useful for sensing cardiac activity. 
     Updating the vector to monitor and/or track changes may be performed periodically, on demand, at a predetermined time, upon the occurrence of a predetermined event, continuously, or as otherwise desired. For example, a PIMD may regularly perform an update of the sense vector used for cardiac discrimination, to keep performance of the PIMD improved and/or adjusted and/or optimized and/or to track or monitor progression of changes. Updating may be useful, for example, when pathology, therapy, posture, or other system or patient change suggests a change in vector may be detected and/or useful. 
     For example, in an APM environment such as described previously, a PIMD in accordance with the present invention may have a controller and communications circuitry that transmits its cardiac composite signals to a bedside signal processor when the patient is asleep. The signal processor may perform a blind source separation and analysis of the composite signals during the patient&#39;s sleep cycle. The signal processor may then determine the appropriate vector or vectors for the PIMD, and reprogram the PIMD before the patient awakes. The PIMD may then operate with the latest programming until the next update. 
       FIG. 16  illustrates further embodiments of a signal source separation process in greater detail, including some optional elements. Entry of the process at block  1622  provides access to a pre-processing facility  1612 , illustrated here as including a covariance matrix computation block  1624  and/or a pre-filtering block  1626  such as, for example, a band-pass filtering block. The composite signals processed at pre-processing block  1612  are provided to a signal source separation block  1615 , which may include functionality of the source separation block  1614  and iterative source separation block  1616  shown in  FIG. 15 . 
     The signal source separation block  1615  includes a principal component analysis block  1628 , which produces an associated set of eigenvectors and eigenvalues using a covariance matrix or composite signals provided by pre-processing block  1612 . A determination  1630  is made as to whether one eigenvalue is significantly larger than any others in the set, making the dimension associated with this eigenvalue a likely candidate for association with the direction along which the power of the signal is maximized. If such a candidate is identified at block  1630 , the candidate signal may immediately be separated  1631  and a determination  1633  made to confirm whether the candidate signal is a cardiac signal, before returning  1644  to the master PIMD routine that called the signal source separation process. 
     If there is no clear candidate eigenvalue, or if the largest value eigenvalue did not provide a signal of interest, an iterative process may be used to separate  1632  and search  1636  for the signal of interest (e.g., cardiac signal). This process  1632 ,  1636 ,  1634  may be repeated until such a signal is found, or no more signals are separable  1634  as determined by exceeding a predefined number of iterations N max  or some other termination criterion. An example of such a criterion is an eigenvalue considered at the current iteration being proportionately smaller than the largest eigenvalues by some predetermined amount. 
     If the iterations  1634  are completed and a cardiac signal is not found at  1636 , then an Independent component analysis  1635  may be attempted to further process the signals in an attempt to find the cardiac signal. If a cardiac signal is still not found at decision  1637 , after exhausting all possibilities, then a set of default settings  1639  may be used, or an error routine may be initiated. 
     In another embodiment of the present invention, a method of signal separation involves sensing, at least in part implantably, two or more composite signals using three or more cardiac electrodes or electrode array elements. The method may further involve performing a source separation using the detected composite signals, the source separation producing two or more vectors. A first vector and a second vector may be selected from the set of vectors. 
     The use of the terms first and second vector are not intended to imply that the vectors are the first and second vectors separated from the composite signal, but that a first vector and a second vector are selected from among any vectors available for a given composite signal. First and second signals may be identified from the detected two or more composite signals using the first and second vectors respectively. The method then involves selecting either the first vector or the second vector as a selected vector based on a selection criterion. 
     Selection criteria may include finding the optimum vector for cardiac signal identification, finding a vector that provides the largest magnitude cardiac signal, or finding another particular signal of interest. For example, the first vector may be selected and used for cardiac activity monitoring, and the second vector may then be selected and used for skeletal muscle activity monitoring. The skeletal muscle signal may then be used to further discriminate arrhythmias from noise such as is further described in commonly owned U.S. Pat. No. 7,117,035, which is hereby incorporated herein by reference. 
     With continued reference to  FIGS. 14 through 16 , one illustrative signal source separation methodology useful with the present invention is described below. Such an approach is particularly well suited for use in a PIMD system. It is to be understood that the example provided below is provided for non-limiting, illustrative purposes only. Moreover, it is understood that signal source separation within the context of the present invention need not be implemented using the specific processes described below, or each and every process described below. 
     A collected signal may be pre-filtered to suppress broadly incoherent noise and to generally optimize the signal-to-noise ratio (SNR). Any noise suppression in this step has the additional benefit of reducing the effective number of source signals that need to be separated. A Principal Component Analysis (PCA) may be performed on the collected and/or pre-filtered signal, producing a set of eigenvectors and associated eigenvalues describing the optimal linear combination, in a least-squares sense, of the recorded signals that makes the components coming from different sources orthogonal to one another. As an intermediate step to performing the PCA, an estimate of the spatial covariance matrix may be computed and averaged over a relatively short time interval (on the order of 2-3 beats), or over the windowed signal as described previously, to enhance those components that are mutually correlated. 
     Each eigenvalue corresponds to the power of the signal projected along the direction of each associated eigenvector. The cardiac signal component is typically identified by one of the largest eigenvalues. Occasionally, PCA does not achieve a substantially sufficient level of source independence. In such a case, an Independent Component Analysis (ICA) may be performed to determine the actual source direction, either upon the PCA-transformed signal, or directly upon the collected signal. The ICA consists of a unitary transformation based on higher-order statistical analysis. 
     For example, separation of two mixed sources may be achieved by rotating the complex variable formed from the signals on an angle that aligns their probability distributions with basis vectors. In another approach, an algorithm based on minimization of mutual information between components, as well as other approaches generally known in the field of ICA, may be used to achieve reconstructed source independence. 
     A PIMD may, for example, employ a hierarchical decision-making procedure that initiates a blind source separation algorithm upon the detection of a condition under which the target vector may change. By way of example, a local peak density algorithm or a curvature-based significant point methodology may be used as a high-level detection routine. Other sensors/information available to the PIMD may also trigger the initiation of a blind source separation algorithm. 
     The PIMD may compute an estimate of the covariance matrix. It may be sufficient to compute the covariance matrix for only a short time. Computation of the eigenvalues and eigenvectors required for the PCA may also be performed adaptively through an efficient updating algorithm. 
     The cardiac signal may be identified among the few (e.g., two or three) largest separated signals. One of several known algorithms may be used. For example, local peak density (LPD) or beat detection (BD) algorithms may be used. The LPD algorithm may be used to identify the cardiac signal by finding a signal that has an acceptable physiologic range of local peak densities by comparing the LPD to a predetermined range of peak densities known to be acceptable. The BD algorithm finds a signal that has a physiologic range of beat rate. In the case where two signals look similar, a morphology algorithm may be used for further discrimination. It may be beneficial to use the same algorithm at different levels of hierarchy: 1) initiation of blind source separation algorithm; 2) iterative identification of a cardiac signal. 
     Mathematical development of an example of blind source separation algorithm in accordance with the present invention is provided as follows. Assume there are m source signals s 1 (t), . . . , s m (t) that are detected inside of the body, including a desired cardiac signal and some other independent noise, which may, for example, include myopotential noise, electrocautery response, etc. These signals are recorded simultaneously from k sensing vectors derived from subcutaneous sensing electrodes, where all m signals may be resolved if k&gt;m. By definition, the signals are mixed together into the overall voltage gradient sensed across the electrode array. In addition, there is usually an additive noise attributable, for example, to environmental noise sources. The relationship between the source signals s(t) and recorded signals x(t) is described below: 
     
       
         
           
             
               
                 ( 
                 
                   
                     
                       
                         
                           x 
                           1 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       
                         
                           x 
                           2 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           x 
                           k 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                 
                 ) 
               
               = 
               
                 
                   
                     ( 
                     
                       
                         
                           
                             
                               y 
                               1 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                       
                         
                           
                             
                               y 
                               2 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                       
                         
                           ⋮ 
                         
                       
                       
                         
                           
                             
                               y 
                               k 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                     
                     ) 
                   
                   + 
                   
                     ( 
                     
                       
                         
                           
                             
                               n 
                               1 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                       
                         
                           
                             
                               n 
                               2 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                       
                         
                           ⋮ 
                         
                       
                       
                         
                           
                             
                               n 
                               k 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                     
                     ) 
                   
                 
                 = 
                 
                   
                     
                       
                         ( 
                         
                           
                             
                               
                                 a 
                                 11 
                               
                             
                             
                               
                                 a 
                                 12 
                               
                             
                             
                               ⋯ 
                             
                             
                               
                                 a 
                                 
                                   1 
                                   ⁢ 
                                   m 
                                 
                               
                             
                           
                           
                             
                               
                                 a 
                                 21 
                               
                             
                             
                               
                                 a 
                                 22 
                               
                             
                             
                               ⋯ 
                             
                             
                               
                                 a 
                                 
                                   2 
                                   ⁢ 
                                   m 
                                 
                               
                             
                           
                           
                             
                               ⋮ 
                             
                             
                               ⋮ 
                             
                             
                               ⋱ 
                             
                             
                               ⋮ 
                             
                           
                           
                             
                               
                                 a 
                                 
                                   k 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   1 
                                 
                               
                             
                             
                               
                                 a 
                                 
                                   k 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   2 
                                 
                               
                             
                             
                               ⋯ 
                             
                             
                               
                                 a 
                                 
                                   k 
                                   ⁢ 
                                   
                                       
                                   
                                   ⁢ 
                                   m 
                                 
                               
                             
                           
                         
                         ) 
                       
                       ⁢ 
                       
                         ( 
                         
                           
                             
                               
                                 
                                   s 
                                   1 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   t 
                                   ) 
                                 
                               
                             
                           
                           
                             
                               
                                 
                                   s 
                                   2 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   t 
                                   ) 
                                 
                               
                             
                           
                           
                             
                               ⋮ 
                             
                           
                           
                             
                               
                                 
                                   s 
                                   m 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   t 
                                   ) 
                                 
                               
                             
                           
                         
                         ) 
                       
                     
                     + 
                     
                       ( 
                       
                         
                           
                             
                               
                                 n 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             
                               
                                 n 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             ⋮ 
                           
                         
                         
                           
                             
                               
                                 n 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                       x 
                       ⁡ 
                       
                         ( 
                         t 
                         ) 
                       
                     
                     = 
                     
                       
                         
                           y 
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                         + 
                         
                           n 
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                       = 
                       
                         
                           As 
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                         + 
                         
                           n 
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                     
                   
                 
               
             
             , 
             
               m 
               &lt; 
               k 
             
           
         
       
     
     Here, x(t) is an instantaneous linear mixture of the source signals and additive noise, y(t) is the same linear mixture without the additive noise, n(t) is environmental noise modeled as Gaussian noise, A is an unknown mixing matrix, and s(t) are the unknown source signals considered here to include the desired cardiac signal and other biological artifacts. There is no assumption made about the underlying structure of the mixing matrix and the source signals, except for their spatial statistical independence. The objective is to reconstruct the source signals s(t) from the recorded signals x(t). 
     Reconstruction of the source signals s(t) from the recorded signals x(t) may involve pre-filtering x(t) to optimize the SNR (i.e., maximize the power of s(t) against that of n(t)). Here, a linear phase filter may be used to minimize time-domain dispersion (tails and ringing) and best preserve the underlying cardiac signal morphology. It is noted that the notation x(t) is substituted for the pre-filtered version of x(t). 
     An estimate of the spatial covariance matrix R is formed as shown immediately below. This step serves to enhance the components of the signal that are mutually correlated and downplays incoherent noise. 
     
       
         
           
             R 
             = 
             
               
                 
                   1 
                   
                     T 
                     
                       ( 
                       
                         ∼ 
                         
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           sec 
                         
                       
                       ) 
                     
                   
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       
                         t 
                         = 
                         1 
                       
                       , 
                       T 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     
                       ( 
                       
                         
                           
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             … 
                           
                         
                         
                           
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                       
                       ) 
                     
                     * 
                     
                       ( 
                       
                         
                           
                             x 
                             1 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             x 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         … 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             x 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                       ) 
                     
                   
                 
               
               = 
               
                 
                   1 
                   
                     T 
                     
                       ( 
                       
                         ∼ 
                         
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           sec 
                         
                       
                       ) 
                     
                   
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       
                         t 
                         = 
                         1 
                       
                       , 
                       T 
                     
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     [ 
                     
                       
                         
                           
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           … 
                         
                         
                           
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                       
                       
                         
                           
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           … 
                         
                         
                           
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                       
                       
                         
                           … 
                         
                         
                           … 
                         
                         
                           ⋱ 
                         
                         
                           … 
                         
                       
                       
                         
                           
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 1 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                         
                           … 
                         
                         
                           
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             * 
                             
                               
                                 x 
                                 k 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                         
                       
                     
                     ] 
                   
                 
               
             
           
         
       
     
     Eigenvalues and eigenvectors of the covariance matrix R may be determined using singular value decomposition (SVD). By definition, the SVD factors R as a product of three matrices R=USV T , where U and V are orthogonal matrices describing amplitude preserving rotations, and S is a diagonal matrix that has the squared eigenvalues σ 1  . . . σ k  on the diagonal in monotonically decreasing order. Expanded into elements, this SVD may be expressed as follows. 
     
       
         
           
             R 
             = 
             
               
                 ( 
                 
                   
                     
                       
                         u 
                         11 
                       
                     
                     
                       
                         u 
                         12 
                       
                     
                     
                       … 
                     
                     
                       
                         u 
                         
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                   
                     
                       
                         u 
                         21 
                       
                     
                     
                       
                         u 
                         22 
                       
                     
                     
                       … 
                     
                     
                       
                         u 
                         
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                     
                       ⋮ 
                     
                     
                       ⋱ 
                     
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         u 
                         
                           k 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                         
                       
                     
                     
                       
                         u 
                         
                           k 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                       
                     
                     
                       … 
                     
                     
                       
                         u 
                         kk 
                       
                     
                   
                 
                 ) 
               
               ⁢ 
               
                 ( 
                 
                   
                     
                       
                         σ 
                         1 
                       
                     
                     
                       0 
                     
                     
                       0 
                     
                     
                       0 
                     
                   
                   
                     
                       0 
                     
                     
                       
                         σ 
                         2 
                       
                     
                     
                       0 
                     
                     
                       0 
                     
                   
                   
                     
                       ⋮ 
                     
                     
                       ⋮ 
                     
                     
                       ⋱ 
                     
                     
                       ⋮ 
                     
                   
                   
                     
                       0 
                     
                     
                       0 
                     
                     
                       … 
                     
                     
                       
                         σ 
                         k 
                       
                     
                   
                 
                 ) 
               
               ⁢ 
               
                 ( 
                 
                   
                     
                       
                         v 
                         11 
                       
                     
                     
                       
                         v 
                         12 
                       
                     
                     
                       … 
                     
                     
                       
                         v 
                         
                           1 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                   
                     
                       
                         v 
                         21 
                       
                     
                     
                       
                         v 
                         22 
                       
                     
                     
                       … 
                     
                     
                       
                         v 
                         
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                     
                       ⋮ 
                     
                     
                       ⋱ 
                     
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         v 
                         
                           k 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                         
                       
                     
                     
                       
                         v 
                         
                           k 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           2 
                         
                       
                     
                     
                       … 
                     
                     
                       
                         v 
                         kk 
                       
                     
                   
                 
                 ) 
               
             
           
         
       
     
     The columns of matrix V consist of eigenvectors that span a new coordinate system wherein the components coming from different sources are orthogonal to one another. Eigenvalues σ 1  . . . σk correspond respectively to columns 1 . . . k of V. Each eigenvalue defines the signal “power” along the direction of its corresponding eigenvector. The matrix V thus provides a rotational transformation of x(t) into a space where each separate component of x is optimally aligned, in a least-squares sense, with a basis vector of that space. 
     The largest eigenvalues correspond to the highest power components, which typically represent the mixed source signals y 1 (t), . . . , y m (t). The lower eigenvalues typically are associated with additive noise n 1 (t), . . . , n k-m (t). Each eigenvector may then be viewed as an optimal linear operator on x that maximizes the power of the corresponding independent signal component. As a result, the transformed signal is found as: 
     
       
         
           
             
               
                 y 
                 ^ 
               
               ⁡ 
               
                 ( 
                 t 
                 ) 
               
             
             = 
             
               
                 ( 
                 
                   
                     
                       
                         
                           
                             y 
                             ^ 
                           
                           1 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           
                             y 
                             ^ 
                           
                           m 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                 
                 ) 
               
               = 
               
                 
                   ( 
                   
                     
                       
                         
                           v 
                           11 
                         
                       
                       
                         
                           v 
                           12 
                         
                       
                       
                         … 
                       
                       
                         
                           v 
                           
                             1 
                             ⁢ 
                             k 
                           
                         
                       
                     
                     
                       
                         ⋮ 
                       
                       
                         ⋮ 
                       
                       
                         ⋱ 
                       
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           v 
                           
                             1 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             m 
                           
                         
                       
                       
                         
                           v 
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             m 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           v 
                           km 
                         
                       
                     
                   
                   ) 
                 
                 * 
                 
                   ( 
                   
                     
                       
                         
                           
                             x 
                             1 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                     
                     
                       
                         
                           
                             x 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                     
                     
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           
                             x 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             t 
                             ) 
                           
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     The component estimates ŷ 1 (t), . . . , ŷ m (t) of ŷ 1 (t), . . . , ŷ m (t) are aligned with the new orthogonal system of coordinates defined by eigenvectors. As a result, they should be orthogonal to each other and thus independent. 
     In an alternative implementation, eigenvalues and eigenvectors of the covariance matrix R may be determined using eigenvalue decomposition (ED). By definition, the ED solves the matrix equation RV=SV so that S is a diagonal matrix having the eigenvalues σ 1  . . . σ k  on the diagonal, in monotonically decreasing order, and so that matrix V contains the corresponding eigenvectors along its columns. The resulting eigenvalues and associated eigenvectors may be applied in similar manner to those resulting from the SVD of covariance matrix R. 
     In an alternative implementation, eigenvalues and eigenvectors are computed directly from x(t) by forming a rectangular matrix X of k sensor signals collected during a time segment of interest, and performing an SVD directly upon X. The matrix X and its decomposition may be expressed as follows. 
     
       
         
           
             X 
             = 
             
               
                 ( 
                 
                   
                     
                       
                         
                           x 
                           1 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       
                         
                           x 
                           2 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           x 
                           k 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                 
                 ) 
               
               = 
               
                 
                   ( 
                   
                     
                       
                         
                           
                             x 
                             1 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               1 
                             
                             ) 
                           
                         
                       
                       
                         
                           
                             x 
                             1 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               2 
                             
                             ) 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           
                             x 
                             1 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               T 
                             
                             ) 
                           
                         
                       
                     
                     
                       
                         
                           
                             x 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               1 
                             
                             ) 
                           
                         
                       
                       
                         
                           
                             x 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               2 
                             
                             ) 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           
                             x 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               T 
                             
                             ) 
                           
                         
                       
                     
                     
                       
                         ⋮ 
                       
                       
                         ⋮ 
                       
                       
                         ⋱ 
                       
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           
                             x 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               1 
                             
                             ) 
                           
                         
                       
                       
                         
                           
                             x 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               2 
                             
                             ) 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           
                             x 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             
                               t 
                               T 
                             
                             ) 
                           
                         
                       
                     
                   
                   ) 
                 
                 = 
                 
                   USV 
                   T 
                 
               
             
           
         
       
     
     Note that in cases where T&gt;k, a so-called “economy-size” SVD may be used to find the eigenvalues and eigenvectors efficiently. Such an SVD may be expressed as follows, expanded into elements. 
     
       
         
           
             X 
             = 
             
               
                 USV 
                 T 
               
               = 
               
                 
                   ( 
                   
                     
                       
                         
                           u 
                           11 
                         
                       
                       
                         
                           u 
                           12 
                         
                       
                       
                         … 
                       
                       
                         
                           u 
                           
                             1 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             T 
                           
                         
                       
                     
                     
                       
                         
                           u 
                           21 
                         
                       
                       
                         
                           u 
                           22 
                         
                       
                       
                         … 
                       
                       
                         
                           u 
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             T 
                           
                         
                       
                     
                     
                       
                         ⋮ 
                       
                       
                         ⋮ 
                       
                       
                         ⋱ 
                       
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           u 
                           
                             k 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                           
                         
                       
                       
                         
                           u 
                           
                             k 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             2 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           u 
                           kT 
                         
                       
                     
                   
                   ) 
                 
                 ⁢ 
                 
                   ( 
                   
                     
                       
                         
                           σ 
                           1 
                         
                       
                       
                         0 
                       
                       
                         … 
                       
                       
                         0 
                       
                     
                     
                       
                         0 
                       
                       
                         
                           σ 
                           2 
                         
                       
                       
                         … 
                       
                       
                         0 
                       
                     
                     
                       
                         ⋮ 
                       
                       
                         ⋮ 
                       
                       
                         ⋱ 
                       
                       
                         ⋮ 
                       
                     
                     
                       
                         0 
                       
                       
                         0 
                       
                       
                         … 
                       
                       
                         
                           σ 
                           k 
                         
                       
                     
                   
                   ) 
                 
                 ⁢ 
                 
                   ( 
                   
                     
                       
                         
                           v 
                           11 
                         
                       
                       
                         
                           v 
                           12 
                         
                       
                       
                         … 
                       
                       
                         
                           v 
                           
                             1 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                       
                     
                     
                       
                         
                           v 
                           21 
                         
                       
                       
                         
                           v 
                           22 
                         
                       
                       
                         … 
                       
                       
                         
                           v 
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                       
                     
                     
                       
                         ⋮ 
                       
                       
                         ⋮ 
                       
                       
                         ⋱ 
                       
                       
                         ⋮ 
                       
                     
                     
                       
                         
                           v 
                           
                             k 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                           
                         
                       
                       
                         
                           v 
                           
                             k 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             2 
                           
                         
                       
                       
                         … 
                       
                       
                         
                           v 
                           kk 
                         
                       
                     
                   
                   ) 
                 
               
             
           
         
       
     
     A similar economy-sized SVD may also be used for the less typical case where k&gt;T. The matrices S and V resulting from performing the SVD of data matrix X may be applied in the context of this present invention identically as the matrices S and V resulting from performing the SVD on the covariance matrix R. 
     At this point, the mutual separation of ŷ 1 (t), . . . , ŷ m (t) would be completed, based on the covariance statistics. Occasionally, information from covariance is not sufficient to achieve source independence. This happens, for example, when the cardiac signal is corrupted with electrocautery, which may cause perturbations from the linearly additive noise model. In such a case, Independent Component Analysis (ICA) may be used to further separate the signals. 
     The ICA seeks to find a linear transformation matrix W that inverts the mixing matrix A in such manner as to recover an estimate of the source signals. The operation may be described as follows. 
     
       
         
           
             
               s 
               ⁡ 
               
                 ( 
                 t 
                 ) 
               
             
             = 
             
               
                 ( 
                 
                   
                     
                       
                         
                           s 
                           1 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       
                         
                           s 
                           2 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                   
                     
                       ⋮ 
                     
                   
                   
                     
                       
                         
                           s 
                           m 
                         
                         ⁡ 
                         
                           ( 
                           t 
                           ) 
                         
                       
                     
                   
                 
                 ) 
               
               = 
               
                 
                   Wy 
                   ⁡ 
                   
                     ( 
                     t 
                     ) 
                   
                 
                 ≈ 
                 
                   
                     A 
                     
                       - 
                       1 
                     
                   
                   ⁢ 
                   
                     y 
                     ⁡ 
                     
                       ( 
                       t 
                       ) 
                     
                   
                 
               
             
           
         
       
     
     Here we substitute s(t) for the recovered estimate of the source signals. The signal vector y(t) corresponds to either the collected sensor signal vector x(t) or to the signal ŷ(t) separated with PCA. The matrix W is the solution of an optimization problem that maximizes the independence between the components s 1 (t), . . . , s m (t) of s(t)=Wy(t). We treat the components of s(t) as a vector of random variables embodied in the vector notation s, so that the desired transformation would optimize some cost function C(s)=C([s 1 (t), . . . , s m (t)]) that measures the mutual independence of these components. Given the joint probability density function (pdf) ƒ(s) and the factorized pdf  ƒ (s)=ƒ 1 (s 1 )ƒ 2 (s 2 ) . . . ƒ m (s m ), or given estimates of these pdf&#39;s, we may solve the following. 
     
       
         
           
             
               
                 min 
                 W 
               
               ⁢ 
               
                 C 
                 ⁡ 
                 
                   ( 
                   s 
                   ) 
                 
               
             
             = 
             
               
                 min 
                 W 
               
               ⁢ 
               
                 ∫ 
                 
                   
                     D 
                     ⁡ 
                     
                       ( 
                       
                         
                           f 
                           ⁡ 
                           
                             ( 
                             s 
                             ) 
                           
                         
                         , 
                         
                           
                             f 
                             _ 
                           
                           ⁡ 
                           
                             ( 
                             s 
                             ) 
                           
                         
                       
                       ) 
                     
                   
                   ⁢ 
                   
                     ⅆ 
                     s 
                   
                 
               
             
           
         
       
     
     The function D(ƒ(s),  ƒ (s)) may be understood as a standard distance measure generally known in the art, such as for example an absolute value difference |ƒ(s)−  ƒ (s)|, Euclidean distance (ƒ(s)−  ƒ (s)) 2 , or p-norm (ƒ(s)−  ƒ (s) p . The distance measure approaches zero as ƒ(s) approaches  ƒ (s), which by the definition of statistical independence, occurs as the components of s approach mutual statistical independence. 
     In an alternative implementation, the distance measure may take the form of a Kullback-Liebler divergence (KLD) between ƒ(s) and  ƒ (s), yielding cost function optimizations in either of the following forms. 
     
       
         
           
             
               
                 
                   
                     
                       
                         min 
                         W 
                       
                       ⁢ 
                       
                         C 
                         ⁡ 
                         
                           ( 
                           s 
                           ) 
                         
                       
                     
                     = 
                     
                       
                         min 
                         W 
                       
                       ⁢ 
                       
                         ∫ 
                         
                           
                             f 
                             ⁡ 
                             
                               ( 
                               s 
                               ) 
                             
                           
                           ⁢ 
                           log 
                           ⁢ 
                           
                             
                               f 
                               ⁡ 
                               
                                 ( 
                                 s 
                                 ) 
                               
                             
                             
                               
                                 f 
                                 _ 
                               
                               ⁡ 
                               
                                 ( 
                                 s 
                                 ) 
                               
                             
                           
                           ⁢ 
                           
                             ⅆ 
                             s 
                           
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   or 
                 
               
             
             
               
                 
                   = 
                   
                     
                       min 
                       W 
                     
                     ⁢ 
                     
                       ∫ 
                       
                         
                           
                             f 
                             _ 
                           
                           ⁡ 
                           
                             ( 
                             s 
                             ) 
                           
                         
                         ⁢ 
                         log 
                         ⁢ 
                         
                           
                             
                               f 
                               _ 
                             
                             ⁡ 
                             
                               ( 
                               s 
                               ) 
                             
                           
                           
                             f 
                             ⁡ 
                             
                               ( 
                               s 
                               ) 
                             
                           
                         
                         ⁢ 
                         
                           ⅆ 
                           s 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     Since the KLD is not symmetric, the two alternative measures are related but not precisely equal. One measure could be chosen, for example, if a particular underlying data distribution favors convergence with that measure. 
     Several alternative approaches may be used to measure the mutual independence of the components of s. These may include the maximum likelihood method, maximization of negentropy or its approximation, and minimization of mutual information. 
     In the maximum likelihood method, the desired matrix W is found as a solution of the following optimization problem, 
     
       
         
           
             
               
                 
                   
                     max 
                     W 
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         j 
                         = 
                         1 
                       
                       T 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         m 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             f 
                             i 
                           
                           ⁡ 
                           
                             ( 
                             
                               
                                 s 
                                 i 
                               
                               ⁡ 
                               
                                 ( 
                                 
                                   t 
                                   j 
                                 
                                 ) 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 + 
                 
                   T 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   log 
                 
               
               | 
               
                 det 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 W 
               
               | 
             
             = 
             
               
                 
                   
                     max 
                     W 
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         j 
                         = 
                         1 
                       
                       T 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         m 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           
                             f 
                             i 
                           
                           ⁡ 
                           
                             ( 
                             
                               
                                 w 
                                 i 
                                 T 
                               
                               ⁢ 
                               
                                 y 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     t 
                                     j 
                                   
                                   ) 
                                 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
                 + 
                 
                   T 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   log 
                 
               
               | 
               
                 det 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 W 
               
               | 
             
           
         
       
     
     where w i  are columns of the matrix W. In the negentropy method, the cost function is defined in terms of differences in entropy between s and a corresponding Gaussian random variable, resulting in the following optimization problem, 
                 max   W     ⁢     {       H   ⁡     (     s   gauss     )       -     H   ⁡     (   s   )         }       =       max   W     ⁢     {       -     ∫       f   ⁡     (     s   gauss     )       ⁢   log   ⁢           ⁢     f   ⁡     (     s   gauss     )       ⁢     ⅆ     s   gauss             +     ∫       f   ⁡     (   s   )       ⁢     log   ⁡     (   s   )       ⁢     ⅆ   s           }             
where H(s) is the entropy of random vector s, and s gauss  is a Gaussian random vector chosen to have a covariance matrix substantially the same as that of s.
 
     In the minimization of mutual information method, the cost function is defined in terms of the difference between the entropy of s and the sum of the individual entropies of the components of s, resulting in the following optimization problem 
     
       
         
           
             
               min 
               W 
             
             ⁢ 
             
               { 
               
                 
                   - 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       m 
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       ∫ 
                       
                         
                           f 
                           ⁡ 
                           
                             ( 
                             
                               s 
                               i 
                             
                             ) 
                           
                         
                         ⁢ 
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           f 
                           ⁡ 
                           
                             ( 
                             
                               s 
                               i 
                             
                             ) 
                           
                         
                         ⁢ 
                         
                           ⅆ 
                           
                             s 
                             i 
                           
                         
                       
                     
                   
                 
                 + 
                 
                   ∫ 
                   
                     
                       f 
                       ⁡ 
                       
                         ( 
                         s 
                         ) 
                       
                     
                     ⁢ 
                     log 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       f 
                       ⁡ 
                       
                         ( 
                         s 
                         ) 
                       
                     
                     ⁢ 
                     
                       ⅆ 
                       s 
                     
                   
                 
               
               } 
             
           
         
       
     
     All preceding cost function optimizations having an integral form may be implemented using summations by approximating the underlying pdf&#39;s with discrete pdf&#39;s, for example as the result of estimating the pdf using well-known histogram methods. We note that knowledge of the pdf, or even an estimate of the pdf, may be difficult to implement in practice due either to computational complexity, sparseness of available data, or both. These difficulties may be addressed using cost function optimization methods based upon kurtosis, a statistical parameter that does not require a pdf. 
     In an alternative method a measure of independence could be expressed via kurtosis, equivalent to the fourth-order statistic defined as the following for the i th  component of s
 
 kurt ( s   i )= E{s   i   4 }−3( E{s   i   2 }) 2  
 
     In this case W is found as a matrix that maximizes kurtosis of s=Wy over all the components of s (understanding y to be a vector of random variables corresponding to the components of y(t)). In all the previous examples of ICA optimization the solution W could be found via numerical methods such as steepest descent, Newton iteration, etc., well known and established in the art. These methods could prove numerically intensive to implement in practice, particularly if many estimates of statistics in s must be computed for every iteration in W. 
     Computational complexity may be addressed several ways. To begin, the ICA could be performed on the PCA-separated signal ŷ(t) with the dimensionality reduced to only the first few (or in the simplest case, two) principal components. For situations where two principal components are not sufficient to separate the sources, the ICA could still be performed pairwise on two components at a time, substituting component pairs at each iteration of W (or group of iterations of W). 
     In one example, a simplified two-dimensional ICA may be performed on the PCA separated signals. In this case, a unitary transformation could be found as a Givens rotation matrix with rotation angle θ, 
               W   ⁡     (   θ   )       =     (           cos   ⁢           ⁢   θ           sin   ⁢           ⁢   θ                 -   sin     ⁢           ⁢   θ           cos   ⁢           ⁢   θ           )           
where s(t)=W(θ)y(t). Here W(θ) maximizes the probability distribution of each component along the basis vectors, such that the following is satisfied.
 
     
       
         
           
             θ 
             = 
             
               arg 
               ⁢ 
               
                   
               
               ⁢ 
               
                 
                   max 
                   θ 
                 
                 ⁢ 
                 
                   
                     ∑ 
                     
                       t 
                       = 
                       1 
                     
                     T 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     log 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       f 
                       ⁡ 
                       
                         ( 
                         
                           
                             s 
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                           | 
                           θ 
                         
                         ) 
                       
                     
                   
                 
               
             
           
         
       
     
     This optimal rotation angle may be found by representing vectors y(t) and s(t) as complex variables in the polar coordinate form
 
ξ= e   i4θ   E (ρ 4   e   i4φ′ )= e   i4θ   E [( s   1   +is   2 ) 4   ]=e   i4θ (κ 40   s +κ 04   s )
 
 y=y   1   +iy   2   =ρe   iφ   , s=s   1   +is   2   =ρe   iφ′ 
 
and finding the relationships between their angles φ, φ′:φ=φ′+θ, where θ is the rotation that relates the vectors. Then, the angle θ may be found from the fourth order-statistic of a complex variable ξ, where κ s  is kurtosis of the signal s(t).
 
     By definition, source kurtosis is unknown, but may be found based on the fact that the amplitude of the source signal and mixed signals are the same. 
     As a Result,
 
4θ={circumflex over (ξ)} sign({circumflex over (γ)})
 
with γ= E[ρ   4 ]−8=κ 40   s +κ 04   s  and ρ 2   =s   1   2   +s   2   2   =y   1   2   +y   2   2  
 
     In summary, the rotation angle may be estimated as: 
             θ   =       1   4     ⁢     angle   ⁡     (       ξ   ^     ⁢           ⁢     sign   ⁡     (     γ   ^     )         )               
where
 
     
       
         
           
             
               
                 ξ 
                 ^ 
               
               = 
               
                 
                   
                     1 
                     T 
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         
                           t 
                           = 
                           1 
                         
                         , 
                         T 
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         ρ 
                         t 
                         4 
                       
                       ⁢ 
                       
                         ⅇ 
                         
                           i 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           4 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           
                             φ 
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                       
                     
                   
                 
                 = 
                 
                   
                     1 
                     T 
                   
                   ⁢ 
                   
                     
                       ∑ 
                       
                         
                           t 
                           = 
                           1 
                         
                         , 
                         T 
                       
                     
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         ( 
                         
                           
                             
                               y 
                               1 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                           + 
                           
                             
                               iy 
                               2 
                             
                             ⁡ 
                             
                               ( 
                               t 
                               ) 
                             
                           
                         
                         ) 
                       
                       4 
                     
                   
                 
               
             
             , 
             
               
 
             
             ⁢ 
             
               
                 γ 
                 ^ 
               
               = 
               
                 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           
                             t 
                             = 
                             1 
                           
                           , 
                           T 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         ρ 
                         t 
                         4 
                       
                     
                   
                   - 
                   8 
                 
                 = 
                 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           
                             t 
                             = 
                             1 
                           
                           , 
                           T 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         
                           ( 
                           
                             
                               
                                 y 
                                 1 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                             + 
                             
                               
                                 iy 
                                 2 
                                 2 
                               
                               ⁡ 
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                           ) 
                         
                         4 
                       
                     
                   
                   - 
                   8 
                 
               
             
           
         
       
     
     After the pre-processing step, the cardiac signal is normally the first or second most powerful signal. In addition, there is usually in practice only one source signal that is temporally white. In this case, rotation of the two-dimensional vector y=y 1 +iy 2 =ρe iφ  is all that is required. In the event that more than two signals need to be separated, the Independent Component Analysis process may be repeated in pair-wise fashion over the m(m−1)/2 signal pairs until convergence is reached, usually taking about (1+√{square root over (m))} iterations. 
     A PIMD that implements the above-described processes may robustly separate the cardiac signal from a low SNR signal recorded from the implantable device. Such a PIMD robustly separates cardiac signals from noise to allow for improved sensing of cardiac rhythms and arrhythmias. 
     The system operates by finding a combination of the spatially collected low SNR signals that makes cardiac signal and noise orthogonal to each other (independent). This combination achieves relatively clean extraction of the cardiac signal even from negative SNR conditions. 
     A PIMD may operate in a batch mode or adaptively, allowing for on-line or off-line implementation. To save power, the system may include the option for a hierarchical decision-making routine that uses algorithms known in the art for identifying presence of arrhythmias or noise in the collected signal and initiating the methods of the present invention. 
     Various modifications and additions can be made to the preferred embodiments discussed hereinabove without departing from the scope of the present invention. Accordingly, the scope of the present invention should not be limited by the particular embodiments described above, but should be defined only by the claims set forth below and equivalents thereof.