Abstract:
Detects external noise using a motion sensor signal for example to increase the specificity of arrhythmia detections based on active muscle noise detection. Whenever a motion signal is present that is below or above a certain frequency, for example 5 Hz, or within a certain frequency range, for example 1 to 10 Hz, and/or above a certain amplitude, for example greater than 1 mg, or close to a known motion pattern, then the detection of fast ventricular arrhythmia is suspended. For the detection of slow arrhythmia, for example asystole or syncope, an episode is confirmed when a short lasting motion sensor signal occurs. Uses a motion sensor based signal, for example as obtained from an accelerometer on an implantable electrode lead and/or implantable device.

Description:
This application claims the benefit of U.S. Provisional Patent Application 61/481,756, filed on 3 May 2011, the specification of which is hereby incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     Embodiments of the invention relate to a method to detect external noise using a motion sensor signal for example to increase the specificity of arrhythmia detections based on active muscle noise detection, specifically and not by way of limitation, embodiments improve the specificity of the arrhythmia detection by active detection of lead or device movement associated with electromedical implants. The electromedical implant can, for example, be an appropriately configured implantable loop recorder or ILR for long terming monitoring of electrocardiograms or ECGs or other implantable pacemaker or an implantable cardioverter/defibrillator or ICD, or any combination thereof. 
     2. Description of the Related Art 
     The primary purpose of implantable loop recorders or ILRs is the detection and/or monitoring of cardiac arrhythmia such as ventricular tachycardia or VT, atrial tachycardia or AT, ventricular fibrillation or VF, or asystole or syncope. The detection of these types of arrhythmia episodes is often impaired by cardiac unrelated disturbances such as muscle movement related noise and/or external noise. Due to these types of noise, the evaluation and/or analysis of the subcutaneous electrocardiogram or SECG with respect to detection of arrhythmia episodes is often mislead and unspecific. For example, arm movement leads to muscle noise, which is detected as a high frequent signal and potentially recognized as VT. Another example is the false detection of syncope due to under sensing of low amplitude SECG. 
     Currently, ILRs are sensitive in detecting arrhythmia but not very specific, i.e., are subject to a high number of false positives. These false detections introduce inefficiencies with respect to post analysis logistical efforts to schedule patient visits and significantly impair the diagnostic use of ILRs. Some studies show that overall specificity of ILRs is about 15% and even worse for high ventricular rates where specificity of ILR&#39;s drops to 0.3%. 
     BRIEF SUMMARY OF THE INVENTION 
     At least one embodiments of the invention utilizes a motion sensor based signal to increase the specificity of arrhythmia detections, for example as analyzed in combination with an sECG. Whenever a motion signal is present, for example over a threshold for time and/or amplitude, the detection of fast arrhythmia is suspended in one or more embodiments of the invention. For the detection of slow arrhythmia, for example asystole or syncope, an episode is confirmed when a short lasting motion sensor signal occurs in one or more embodiments. For example, at least one embodiment of the invention combines multiple signals to correctly discriminate arrhythmia from external noise or low amplitude SECG signals. The method uses the motion sensor based signal, for example as obtained from an accelerometer on an implantable electrode lead and/or implantable device, to increase the specificity of arrhythmia detections. In one or more threshold comparison embodiments, whenever a motion signal is present that is below or above a certain frequency, for example 5 Hz, or within a certain frequency range, for example 1 to 10 Hz, and/or above a certain amplitude, for example greater than 1 mg, then the detection of fast ventricular arrhythmia is suspended. In addition, in one or more embodiments, for the detection of slow arrhythmia (asystole, syncope), an episode is confirmed when a short lasting or short duration motion sensor signal occurs. 
     In one exemplary embodiment, an implantable medical device in a hermetically sealed housing implements an embodiment of the invention and includes electrodes to sense cardiac signals, signal analysis element to process the sensed cardiac signals that may for example include amplifiers, analog/digital converters and the like, a detection element to detect cardiac arrhythmias, a comparison or processing element, along with a memory, at least one motion sensor, a power source and an optional telemetry element. The electrodes in one or more embodiments may be implemented with one or more electrode leads that include electrode contacts, as is known in the art of pacemakers for example. In another embodiment, the electrodes are located at or part of the housing. In yet another embodiment, electrodes at an electrode lead and at the housing are used. The motion sensor may be implemented with an acceleration sensor, such as a microelectromechanical or MEMS format accelerometer as known in the art. Small-scale accelerometers may also include gyroscopes for angular rotation determination and use of rotational information is in keeping with the spirit of the invention, whether derived from differencing of two accelerometer vectors or through use of one or more gyroscopes. Suitable acceleration sensors are sensors that detect acceleration in at least one axis or acceleration in multiple movement axes. Calibration of the orientation may be performed after implantation via the optional telemetry unit with the patient lying face up or face down, on one or each side and standing. Once the orientation of the accelerometers in the body is determined, then the patient may be instructed to move the arms and/or legs or other perform other muscle movements that may be detected by the accelerometer or accelerometers and for example saved in memory for later pattern comparison. The stored patterns of movement may be compared with the detected accelerometer values to determine whether to mark an arrhythmia as incorrect or as confirmed in one or more embodiments in addition to the threshold comparisons previously described. 
     Arrhythmia detection of the cardiac signal is performed in any manner, for example based on timing or other analysis of peaks or signal markers or features observed in the sECG as one skilled in the art will appreciate. The signal of the motion sensor is processed to detect motion signal features. Motion signal features may include amplitude, frequency, signal polarity, waveform or the like or the may be detected or otherwise identified by comparison with recorded reference signals, for example previously stored patterns. The motion sensor signals may be analog or digital depending on the specific embodiment of the accelerometer utilized. Once an arrhythmia is detected the motion signal features detected prior and/or during the arrhythmia are analyzed. Depending on the motion signal features, the detected arrhythmia is either marked as incorrect or confirmed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1 : illustrates an ILR having a device body and a flexible lead and showing the orientation axes associated with each accelerometer. 
         FIG. 2 : illustrates a close-up of the flexible lead body having an accelerometer embedded into the lead body. The accelerometer is connected to the signal analysis module via a feedthrough. The feedthrough also connects the sECG electrode that is located at the distal end of the lead to the signal analysis module. 
         FIG. 3 : illustrates a close-up of the feedthrough that provides electrical connection of the accelerometer and sECG lead to the signal analysis module. 
         FIG. 4 : illustrates a logical processing schematic of one embodiment of the signal analysis element or module employed by one or more embodiments of the invention. The signal or data streams from the accelerometer and the sECG, are routed into detection modules that generate peak markers or detect signal features from the sECG and signal frequencies/amplitudes or other motion related characteristics from the accelerometer(s). The arrhythmia detection element analyzes the times of the various peaks in one or more embodiments to recognize potential arrhythmia. The time sequence of the peak markers or signal features of the potential arrhythmia are compared to the time sequence of motion related characteristics in the comparator element or module, which marks the arrhythmia as incorrect or confirmed based on the motion related characteristics. 
         FIG. 5 : illustrates a flow chart for an embodiment of the processing employed by the comparator element. The comparator element analyzes the time of the potential arrhythmia with respect to the motion related characteristics, for example frequency/amplitude/duration and/or pattern and based on the detected heart rate, marks the arrhythmia as incorrect or confirmed. 
         FIG. 6 : illustrates an example of correct arrhythmia detection, wherein the sECG signal indicates an arrhythmia and the motion sensor indicates below threshold motion as determined from either frequency or amplitude or both or any other motion related characteristic. 
         FIG. 7 : illustrates an example of incorrect arrhythmia detection, wherein the sECG signal indicates an arrhythmia and the motion sensor indicates above threshold motion as determined from either frequency or amplitude or both or any other motion related characteristic. 
         FIG. 8 : illustrates an example of confirmed arrhythmia detection, wherein the sECG signal indicates an asystole or syncope and the motion sensor indicates a short duration motion related characteristic. 
         FIG. 9 : illustrates an example of incorrect arrhythmia detection, wherein the sECG signal indicates an asystole or syncope and the motion sensor does not indicate a short duration motion related characteristic. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  illustrates an embodiment of the invention  100  implemented with an ILR having device body  101  and flexible lead body  102  and at least one accelerometer  190   a  and/or  190   b . In at least one embodiment, the implantable medical device includes a hermetically sealed housing implementation of device body  101  and includes signal analysis element  110  to process the sensed cardiac signals that may for example include amplifiers, analog/digital converters and the like, a detection element to detect cardiac arrhythmias, a comparison or processing element as shown in further detail in  FIG. 4 , along with memory  120 , a power source which is not shown for brevity, and an optional telemetry element  140 . The telemetry element for example may be configured to communicate with remote device programmer  150  or any other remote computing element as desired. 
     One or more embodiments may utilize an accelerometer in device body  101  or in flexible lead body  102  or in both locations. Also shown next to each accelerometer is a respective reference frame (X 1 , Y 1  and Z 1 ) and (X 2 , Y 2  and Z 2 ) respectively, that each accelerometer may be calibrated to after implantation when the ILR and flexible lead body are set in a given orientation. Signals originating in flexible lead body  102 , for example electrical signals, enter device body  101  and are processed by signal analysis module  110  and generally a digitized subset thereof may be stored in memory  120 , along with analysis results, arrhythmia detections and/or false arrhythmia detections, or they may be ignored for example. In addition, motion related characteristics from accelerometer  190   a  and/or  190   b  may be stored in memory, for example to aid in determining how much exercise or other movement a patient undergoes over time. Embodiments may also store potential arrhythmia events in memory along with associated motion characteristics for later analysis to determine what types of motions result in false positives. In one or more embodiments of the invention, the time of day may also be stored in order to determine if arrhythmia or false positives thereof occur more often in a particular time of day. If false positives occur more often in a particular time of the day, this may be taken into account when flagging potential arrhythmia or when marking potential arrhythmia as incorrect. In addition, if false positives occur more often in a given body orientation, this information may also be stored in memory and utilized in filtering potential arrhythmia. If actual arrhythmia occur in a particular body orientation more often, then the patient for example may be instructed to avoid that orientation, for example laying on a left side of the body. Embodiments of the invention may be implemented with any other type of implantable device as desired including pacemakers and/or cardioverter/defibrillators. 
     Before processing begins, and generally during implantation or during an office visit, the accelerometers may be calibrated for orientation, so that their particular orientations in the body of a patient are detected via a gravity vector of 1 g that is observed at a particular direction with respect to each accelerometer. Calibration of the orientation may be performed after implantation via the optional telemetry unit  140  and associated remote programming device  150  for example, with the patient lying face up or face down, on one or each side and/or standing. Alternatively, calibration of orientation may occur upon acceptance of physical gestures from the patient or attending physician as described further below. 
     For example, in one or more embodiments, the reference frame may be defined with respect to the body of the patient, with the y-axis pointing out the left side of the patient&#39;s body, the x-axis pointing out of the chest of the patient, and the z-axis point out of the top of the head of the patient. In addition, each accelerometer is assigned a device frame, with the x, y and z axes aligned however they are implanted. Generally, the accelerometer device x, y and z axes are assumed to be orthogonal, however as one skilled in the art will appreciate, this may be accounted for via another matrix transformation calibration step if required. To calibrate the orientation of the accelerometers, the gravity vector is utilized to find the mapping, or rotation matrix Q, between the device frame coordinates for each accelerometer and the patient&#39;s body frame coordinates. This calibration step in effect enables the recovery of orientation of each device inside the patient&#39;s body, post implantation. Each device accelerometer measures g in the respective device frame. Hence, if the patient is placed in a known orientation, then g in the patient&#39;s body frame is known. Although the patient may be oriented on the back, side and standing, by gathering information in two orientations, the other orientation may be derived from a cross product. Hence, with only two body positions, it is possible to calculate the full mapping between the frames of reference. For example, if the patient is placed on their stomach, then g [Body]=gi, wherein i is the x-axis unit vector. External programmer  150  for example may command the signal analysis element  110  for example to sample a three axes acceleration vector, or average one over a period of time for example to eliminate noise. In this case, accelerometer  190   b  may measure g [Device] and obtain three axes vector u. With the patient placed on their left side, g [Body]=gj, wherein j is the y-axis unit vector, at which time again, the external programmer commands the signal analysis element to sample another three axes acceleration vector, or average the vector for example. In this case, the accelerometer may measure g [Device] and obtain three axes vector v. The rotation matrix Q is utilized herein to denote the matrix that converts Device frame coordinates to Body frame coordinates. As such, Qu=gi and Qv=gj. As one skilled in the art will appreciate, using the cross product, (Qu×Qv)=Q(u×v), and hence Q(u×v)=gi×gj=gk, wherein k is the z-axis unit vector, enables the determination of the vertical translation. Thus with only two orientation measurements, it is possible to solve for the rotation matrix Q. For example, if Qinv is used to denote the inverse of Q, then (Qinv)i=(1/g)u, (Qinv)j=(1/g)v and (Qinv)k=(1/g)(u×v). Hence, Qinv is the matrix with columns (1/g)u, (1/g)v and (1/g)(u×v). Thus, by inverting Qinv, the rotational matrix Q is obtained and for example stored in memory  120  for each accelerometer. 
     Alternatively, for embodiments with or without telemetry units, the patient or attending physician may simply physically gesture to the implantable device to calibrate orientations. For example, in one or more embodiments of the invention, the patient or attending physician may physically tap the area of the body three times where the accelerometer is placed to indicate that the first position orientation should be sampled, for example when the patient is positioned with back oriented down. The patient may then stand up and tap the area of the body using the same gesture, for example three times in three seconds to indicate that the second orientation should be sampled. In this scenario, embodiments of the invention may calibrate orientation with or without a telemetry unit or external programmer through physical gestures. Feedback in the form of three small electric pulses to the electrode lead to tingle the area of the patient&#39;s body may be performed to inform the patient that the device orientation has been calibrated. The exemplary gestures are not intended to be limiting in any manner and any pattern of accelerations with respect to time may be utilized to signify that calibration should take place. 
     Once the orientation of the accelerometers in the body is determined and for example a rotation matrix is stored in memory  120  for each accelerometer, then the patient may optionally be instructed to move the arms and/or legs or other perform other muscle movements that may be detected by the accelerometer or accelerometers and for example saved in memory for later pattern comparison as shown in  FIG. 4 . The stored patterns of movement may be compared with the detected accelerometer values or motion characteristics to determine whether to mark an arrhythmia as incorrect or as confirmed in one or more embodiments in addition to the threshold comparisons for example. 
     With the initial orientation rotation matrices stored in memory, any incoming accelerometers values may be translated through a matrix multiply of the rotation matrix to obtain the true directional values according to the body frame of the patient. This enables statistics such as the percentage of time that a patient is lying down versus standing up to be correlated for false positives for example, to further improve the specificity of one or more embodiments of the invention by storing these and other derived motion characteristics for comparison and analysis. The analysis may be performed by the remote programming device, which may be implemented for example with a service center. In one or more embodiments of the invention, two or more accelerometers may be utilized and in this scenario, the patterns and/or motion characteristics from the two or more accelerometers may be compared to patterns in combination to more accurately recognize the current motion. Alternatively, or in combination, the acceleration vectors may be compared to one another or otherwise differenced to determine relative acceleration and to increase the specificity of arrhythmia detection for example while driving, flying and or accelerating or decelerating in any other environment. 
       FIG. 2  illustrates a close-up of flexible lead body  102  having accelerometer  190   b  embedded within flexible lead body  102 . Accelerometer  190   b  is connected to the signal analysis module  110  via feedthrough  201 . Flexible lead body  102  also includes sECG electrode  203  that is also connected to signal analysis module  110  via feedthrough  201 . The subcutaneous ECG signal or sECG is measured between one electrode at the tip of the lead, i.e., sECG electrode  203  and one electrode on the case on the opposite side of the device, i.e., device body electrode  103 , shown as a filled half circle on the left side of device body  101 . In alternative embodiments, sECG electrode  203  may be implemented as a tip electrode, wherein another ring electrode may be utilized to obtain the sECG instead of through use of the device body electrode if desired (not shown for brevity). 
       FIG. 3  illustrates a close-up of feedthrough  201  that provides electrical connections  311 ,  312  and  313  on the flexible lead body side of feedthrough  201  to the sECG electrode  203  and accelerometer  190   b  common and positive respectively, to electrical connections  301 ,  302  and  303  through feedthrough body  304 . Thus, feedthrough  201  enables the internal electronics of the ILR, such as signal analysis module  110  to obtain electrical signals that are generated external to the ILR. Modern accelerometers are very small and may be produced in different form factors, such as microelectromechanical of MEMS format. This results in a voltage generated by accelerometer and/or digital representation thereof. 
       FIG. 4  illustrates a logical processing schematic of one embodiment of signal analysis element or module  110  employed by one or more embodiments of the invention. The use of the terms element and module herein are interchangeable and otherwise synonymous and indicate any type of processing object that may include hardware, shared hardware in combination with or without firmware or software. Any type of element may be utilized so long as the element may detect, compare and indicate arrhythmia events within the required time between heartbeats, which requires minimal hardware and/or software complexity based on the relatively low rate of processing utilized as one skilled in the art will appreciate. In one or more embodiments of the invention, a single processing unit may implement all elements or modules or any combination thereof, for example by time division processing of the various signals and outputs. 
     The signal or data streams from the accelerometer and the sECG, i.e., the sECG signal and the accelerometer signal that travel on electrical connections  311  and  312 / 313  respectively, pass through feedthrough body  304  to electrical connections  301  and  302 / 303  respectively and are routed into optional amplifiers  401  and  411  respectively and to detection modules  402  and  412  respectively that generate peak markers or detect signal features and signal frequencies and/or amplitudes and motion characteristics from the accelerometer(s). The detected movement signal features or motion characteristics may include amplitude, signal polarity, waveform or the like or may be detected by comparison with recorded reference signals previously stored in memory for example. Patterns P 1  and P 2  show acceleration vectors as detected by a three axes accelerometer. Pattern P 1  shows an upward acceleration in the Z axis and Y axis, indicative of an upward and outward movement of the arm near which the accelerometer is placed for example. Pattern P 2  shows an outward and sideward acceleration indicative of a patient in the initial phase of reaching to the side. Both patterns are also shown with the three components of acceleration associated with each axis for example beneath the three-dimensional map for clarity. Comparison of current motion to known patterns may be made in detection module  412  and/or in or with a separate pattern element  419 , either of which for example that calculate and/or access patterns previously stored in memory  120 . Comparison of the current pattern of motion with a series of stored patterns may be performed by checking the peak amplitudes of the various vectors with respect to time and for example signifying a match if the amplitudes and directions match over a predefined percentage of time segments within any or all of the three axes for example. Any other pattern matching algorithm may be utilized in keeping with the spirit of the invention. Processing in detection modules  402  and  412  (or a single module that time division multiplexes processing for example) may be in the analog or digital domain as desired. Specifically, the time sequences of the sEGC markers are analyzed by arrhythmia detection module  404  and processed, or analyzed to find specific sequences that are indicative of different arrhythmias, for example by comparing event intervals within a sequence of events, or in any other manner as one skilled in the art will appreciate. The time sequence of the peak markers or signal features are of the potential arrhythmia are compared against times of motion events or patterns, and based on the values of various motion characteristics, comparator module  403  determines whether the arrhythmia is confirmed or incorrect. 
       FIG. 5  illustrates a flow chart for an embodiment of the processing employed by the comparator element. The comparator element obtains the time of the potential arrhythmia at  501  and obtains motion related characteristics, for example frequency/amplitude/duration and/or pattern at  502 . If the detected heart rate as determined at  503  is fast, then the motion related characteristics are compared to threshold(s) such as frequency/amplitude and/or a pattern at  504  to determine if the motion related characteristics are over threshold and/or a known pattern of movement. If so, the arrhythmia is marked as incorrect, otherwise the arrhythmia is marked as confirmed at  505 . If on the other hand the heart rate is beneath a particular predefined rate, for example under 60 beats per minute, then if the duration of the motion characteristic is beneath a predefined duration as determined at  506 , then the arrhythmia is confirmed at  507 , otherwise the arrhythmia is marked as incorrect. 
       FIG. 6  illustrates an example of correct arrhythmia detection, wherein the sECG signal indicates an arrhythmia and the motion sensor indicates below threshold motion as determined from either frequency or amplitude or both or any other motion related characteristic. 
       FIG. 7  illustrates an example of incorrect arrhythmia detection, wherein the sECG signal indicates an arrhythmia and the motion sensor indicates above threshold motion as determined from either frequency or amplitude or both or any other motion related characteristic. 
       FIG. 8  illustrates an example of confirmed arrhythmia detection, wherein the sECG signal indicates an asystole or syncope and the motion sensor indicates a short duration motion related characteristic. 
       FIG. 9  illustrates an example of incorrect arrhythmia detection, wherein the sECG signal indicates an asystole or syncope and the motion sensor does not indicate a short duration motion related characteristic. In this manner, embodiments of the invention enable high arrhythmia detection specificities through the elimination of false positives associated with muscle movement.