Abstract:
Heart monitor for detecting ectopic beats in an input electrocardiogram signal that includes an electrocardiogram signal input and a morphological signal analyzer connected to the electrocardiogram signal input, the analyzer being adapted to generate a first time series of values representing the input electrocardiogram signal, a second signal analyzer adapted to generate generating a modified time series of values representing a trend of values of the first time series and a comparison stage being adapted to compare the first time series with the modified time series to thus detect ectopic beats. The invention further relates to a method for detecting ectopic beats in an input electrocardiogram signal that includes obtaining an electrocardiogram signal, generating from the electrocardiogram signal a first time series of values representing the input electrocardiogram signal, generating a modified time series of values representing a trend of values of the first time series and comparing the first time series with the modified time series to thus detect ectopic beats.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention generally relates to medical devices that measure cardiac inter-beat intervals and analyze the cardiac inter-beat intervals. More particularly, the present invention relates to a method and apparatus for accurate detection of ectopic beats, robust removal of short and long cardiac inter-beat intervals that are related to ectopic beats, and construction of artifact-free cardiac inter-beat intervals. 
     2. Description of the Related Art 
     The variation of cardiac inter-beat (e.g., PP, RR) intervals results from both rhythmic activity of the heart electrical source and the dynamic properties of the cardiac conduction pathway, both of which are under autonomic control. In normal sinus rhythm, the RR intervals are known to fluctuate at various time scales, a phenomenon known as heart rate variability (HRV), which has been extensively investigated to probe the autonomic nervous activity. On the other hand, structural or functional abnormalities of the cardiac electrical conduction system can lead to cardiac arrhythmias. 
     The RR interval is a preferred choice to represent cardiac inter-beat interval due to easy acquisition of the electrocardiogram (ECG) signals, and the prominent QRS complexes present in these signals. The RR intervals not only can be easily measured from the surface ECG, but also can be measured from the subcutaneous ECG that is recorded by placing electrodes under the skin, or from the intracardiac electrogram (IEGM) that is recorded by inserting electrodes into the heart. Alternatively, the cardiac inter-beat intervals can also be obtained from other types of biosignals that are known to show the same rhythmic variation as the cardiac beats, including but not limited to, the blood pressure signal, the transthoracic impedance signal, the pulse oximeter signal, finger plethysmography signal, etc. 
     Abnormal cardiac intervals are usually evidenced by abrupt increase or decrease of the RR interval (or heart rate). One typical type of abnormal cardiac interval is caused by ectopic beat (EB) of either atrial or ventricular origin, characterized by abrupt shortening of the RR interval as compared to the preceding RR intervals. Another typical type of abnormal cardiac interval is the long pause after the Ectopic Beat. In fact, the short Ectopic Beat interval and the long post-Ectopic Beat pause often occur in tandem, characterized by a pair of short-long RR intervals in the RR interval tachogram. The short-long RR intervals can also repeat, resulting in the so-called bigeminy rhythm with alternating short and long RR intervals. Yet another type of abnormal cardiac interval is caused by consecutive Ectopic Beats, for example, the duplets, the triplets, or non-sustained ventricular tachycardia (NSVT), characterized by multiple consecutive short RR intervals in the tachogram. Yet another type of abnormal cardiac interval is caused by sudden drop of heart rate, for example, in patients with sick sinus syndrome or transient AV block, evidenced by abrupt increase of the RR intervals in the tachogram. Yet abnormal cardiac intervals can also be caused by transient sensing problems, for example, under-sensing of the R wave (resulting in abrupt increase of RR interval), over-sensing of the T wave (resulting in abrupt decrease of RR interval), or sensing of exogenous noise. 
     Detection of abnormal cardiac interval is a crucial step in time series analysis of RR intervals. For example, in Holter ECG analysis, daily Ectopic Beat counter (or Ectopic Beat frequency) is a simple yet important parameter for cardiac arrhythmia risk stratification. In another example, calculation of HRV parameters involves only normal cardiac inter-beat intervals, thus abnormal cardiac intervals must be removed prior to HRV evaluation. In addition, the heart rate turbulence (HRT) quantifies the short-term fluctuation in sinus cycle length that follows a ventricular Ectopic Beat, and has been shown to be a strong predictor of mortality and sudden cardiac death following myocardial infarction. Furthermore, the Ectopic Beat-free RR intervals can also be used to assess the baseline heart rate, its trend, and its circadian pattern. 
     Numerous techniques have been developed for automatic detection of Ectopic Beats from the ECG signals. One typical approach for Ectopic Beat detection is by means of morphological analysis of the ECG signals, based on the observation that ventricular Ectopic Beats typically have different QRS morphology than the normally conducted QRS morphology. However, this approach has several limitations. First, it cannot be used for Ectopic Beat detection from RR intervals only because it requires ECG morphological information. Second, depending on the source of the Ectopic Beats, the QRS morphology of the Ectopic Beats may not be necessarily different than that of the normally conducted QRS complexes. Third, morphology-based Ectopic Beat detection could not be applied to identify other types of abnormal cardiac cycles, for example, long ventricular pauses due to transient AV block or sudden drop of sinus rate. 
     Alternatively, Ectopic Beat detection can be achieved by analyzing time series of cardiac intervals. In implantable cardiac pacemakers and defibrillators, the Ectopic Beat detection is usually achieved by analyzing the atrial-ventricular relationship when sensing electrodes are placed in both atrium (e.g., RA) and ventricle (e.g., RV or LV). For example, a ventricular sense (VS) outside the ventricular refractory period is usually classified as a normal ventricular depolarization if it is preceded by an atrial event (atrial sense, or atrial pace) within a predefined time interval, or a ventricular Ectopic Beat otherwise. 
     In single chamber pacemakers or defibrillators, the Ectopic Beat detection becomes more challenging since atrial-ventricular association or dissociation could not be assessed. Conventionally, the ventricular Ectopic Beat detection is usually achieved by calculating the ventricular prematurity index by comparing each RR interval with the mean or median of previous several RR intervals. Similarly, the atrial Ectopic Beat detection can be achieved by calculating the atrial prematurity index by comparing each PP interval with the mean or median of previous several PP intervals. 
     Several other methods have been proposed for Ectopic Beat detection from time series of RR intervals. Most of these methods involve calculation of mean RR interval (or heart rate), standard deviation of RR intervals (or heart rate), and beat-to-beat difference of RR intervals (or heart rate). Other methods include polynomial fitting of the RR intervals, and median filter of the RR intervals. Based on our experience, none of these methods has satisfactory performance in terms of sensitivity and specificity of Ectopic Beat detection. 
     Morphological operators have been widely used in 2D image processing for noise removal, and have shown to have better edge preservation performance than other linear or nonlinear filters. The morphological operators have very high computation efficiency, and can be implemented in hardware platform, thus they are particularly suitable for application in low-power devices. 
     However, the application of morphological operators in 1D signal processing, in particular the ECG signal processing, has been limited. Morphological operators were used to implement a peak-valley extractor for QRS complex detection in ECG signals. Another morphological approach was developed to detect QRS complexes and remove baseline wander in neonatal ECG signals. Such approach was disclosed in U.S. Pat. No. 5,817,133 issued to Houben, for discriminating P waves from far-field R waves in an implantable pacemaker. 
     BRIEF SUMMARY OF THE INVENTION 
     It is an object of the invention to provide another method and apparatus for Ectopic Beat detection. 
     According to a first aspect of this invention, this object is achieved by a heart monitor for detecting ectopic beats in an input electrocardiogram signal, said heart monitor comprising
         an electrocardiogram signal input and   a (first) signal analyzer connected to said electrocardiogram signal input, said signal analyzer being adapted to generate a first time series of values representing said input electrocardiogram signal,   a second signal analyzer adapted to generate a modified time series of values representing a trend of values of said first time series and   a comparison stage being adapted to compare said first time series with said modified time series to thus detect ectopic beats.       

     The first time series of values representing said input electrocardiogram signal can e.g. be a time series of measured RR-intervals or time series of QRS metrics other than RR intervals or a combination of both. 
     Preferred embodiments include: 
     A heart monitor, wherein the signal analyzer is adapted to generate a time series of values representing QRS metrics (that is the first time series), wherein each value of the time series represents at least one of the QRS metrics, such as the width of the QRS complex, the positive or negative peak amplitude of the QRS complex, the absolute area under the QRS complex, the maximum positive or negative slopes of the QRS complex, the dominant frequency component of the QRS complex, the complexity measures (e.g., sampled entropy) of the QRS complex. The second signal analyzer is preferably adapted to apply morphological operators to the time series of QRS metrics and to remove abnormal QRS metrics from said time series of QRS metrics to thus derive said modified time series representing a trend of said QRS metrics. 
     A heart monitor, wherein the signal analyzer is adapted to generate a first time series of values representing each duration of a respectively measured RR interval, and wherein said second signal analyzer is adapted to apply morphological operators to said first time series of measured RR-intervals and to remove abnormal RR intervals from said first time series of measured RR intervals to thus derive said modified time series of RR-intervals representing a trend of RR intervals. 
     Typically, the comparison stage is adapted to subtract said first time series from said modified time series and to thus generate difference signal values and detect abnormal RR intervals or QRS metrics by comparing said difference signal values to at least one threshold value. 
     Alternatively, the comparison stage can be adapted to generate a series of ratio values by dividing each value of said first time series by its associated value of said modified time series and to compare each ratio value thus derived with at least one threshold value. 
     Preferably, the comparison stage can be adapted to generate both the difference signal values and the ratio values and to compare each difference signal value to at least one threshold value and to compare each ratio value with at least one threshold value. 
     Preferably, the morphological signal analyzer is adapted to generate said modified time series by applying both, an erosion operator and a dilation operator to said first time series to thus obtain said modified time series of values representing a trend of values of said first time series. The erosion operator and the dilation operator both are morphological operators. 
     According to a second aspect of this invention, the object of the invention is achieved by a method for detecting ectopic beats in an input electrocardiogram signal, said heart monitor comprising the steps of
         Obtaining an electrocardiogram signal   Generating from said electrocardiogram signal a first time series of values representing said input electrocardiogram signal,   Generating a modified time series of values representing a trend of values of said first time series and   Comparing said first time series with said modified time series to thus detect ectopic beats.       

     The first time series of values representing said input electrocardiogram signal can e.g. be a time series of measured RR-intervals or time series of QRS metrics other than RR intervals or a combination of both. 
     Preferred methods include: 
     A step of generating a first time series comprises generating a time series of values representing QRS metrics (as the first time series), wherein each value of said modified time series represents at least one of the QRS metrics, such as the width of the QRS complex, the positive or negative peak amplitude of the QRS complex, the absolute area under the QRS complex, the maximum positive or negative slopes of the QRS complex, the dominant frequency component of the QRS complex, the complexity measures (e.g., sampled entropy) of the QRS complex. The method preferably further includes a step of generating a modified time series comprises applying morphological operators to the time series of QRS metrics and removing abnormal QRS metrics from said time series of QRS metrics to thus derive said modified time series representing a trend of said QRS metrics. 
     Alternatively, the step of generating a first time series comprises generating a time series of values representing each duration of a respectively measured RR interval, and the step of generating a modified time series comprises applying morphological operators to said first time series of measured RR-intervals and removing abnormal RR intervals from said first time series of measured RR intervals to thus derive said modified time series of RR-intervals representing a trend of RR intervals. 
     Typically, the step of comparing the first time series with the modified time series comprises subtracting said first time series from said modified time series and to thus generate difference signal values and detecting abnormal RR intervals or QRS metrics by comparing said difference signal values to at least one threshold value. 
     Alternatively, the step of comparing said first time series with said modified time series may comprise generating a series of ratio values by dividing each value of said first time series by its associated value of said modified time series and comparing each ratio value thus derived with at least one threshold value. 
     Preferably, the step of comparing the first time series with the modified time series comprises generating both the difference signal values and the ratio values and to compare each difference signal value to at least one threshold value and to compare each ratio value with at least one threshold value. 
     It is further preferred, that step of generating a modified time series comprises applying both, an erosion operator and a dilation operator to said first time series to thus obtain said modified time series of values representing a trend of values of said first time series. The erosion operator and a dilation operator are both morphological operators. 
     The step of generating a modified time series may further comprise applying an erosion operator followed by a dilation operator that together form an opening operator to suppress peaks in the first time series. 
     Likewise, the step of generating a modified time series may further comprise applying a dilation operator followed by an erosion operator that together form a closing operator to suppress pits in the first time series. 
     According to this invention, a first morphological filter forming the second signal analyzer is applied to the RR intervals to remove abrupt short and abrupt long RR intervals, and obtain Ectopic Beat-free RR intervals. The filtered out abrupt short and abrupt long RR intervals are compared to the Ectopic Beat-free RR intervals to identify the Ectopic Beat cycles. 
     According to another embodiment of the present invention, the Ectopic Beat detection is achieved by applying morphological operators to time series of QRS metrics (other than RR intervals) measured from the ECG signals. Preferably, the QRS metrics measures the morphology of the QRS complex, including but are not limited to, the peak amplitude, the width, the absolute area, the positive and negative slopes, etc. Alternatively, the QRS metrics measures the frequency content of the QRS complex. 
     Also according to a preferred embodiment of this invention, a second morphological filter forming a third signal analyzer is applied to the Ectopic Beat-free RR intervals to further detect multiple cycles of consecutive short RR intervals (e.g., non-sustained ventricular tachycardia) or multiple cycles of consecutive long RR intervals (e.g., paroxysmal sinus bradycardia). 
     The filtered RR intervals are used to evaluate the heart rate trend, and heart rate variability. The detected Ectopic Beats are further used for arrhythmia risk stratification, for example, to calculate the Ectopic Beat statistics and post-Ectopic Beat heart rate turbulence. 
     The present invention provides a novel means to detect Ectopic Beat cycles, to remove short and long RR intervals induced by Ectopic Beat, to detect non-sustained short or long RR intervals, and to obtain Ectopic Beat-free RR intervals. Compared to other methods, the morphological filter has better performance in Ectopic Beat detection in terms of accuracy and computation complexity. 
     The details of the invention can be understood from the following drawings and the corresponding text descriptions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other aspects, features and advantages of the present invention will be more apparent from the following more particular description thereof, presented in conjunction with the following drawings wherein: 
         FIG. 1  shows a block diagram of an implantable device for subcutaneous ECG monitoring, and its interfaces with an external programming device and an external portable device, which further communicates with the remote service center. 
         FIG. 2A  shows the circuit block diagram for implementing the erosion operator, and 
         FIG. 2B  shows the circuit block diagram for implementing of the dilation operator. 
         FIG. 3A  shows the block diagram of the opening operation, and 
         FIG. 3B  shows the block diagram the closing operation. 
         FIG. 4A  shows the block diagram of an impulse filter consisting of an opening operation followed by a closing operation, 
         FIG. 4B  shows the block diagram of another impulse filter consisting of a closing operation followed by an opening operation, and 
         FIG. 4C  shows yet another block diagram of an impulse filter in which the opening-closing pair and the closing-opening pair operate in parallel. 
         FIG. 5  shows the block diagram of applying a morphological impulse filter to the input RR intervals to obtain the filtered fRR intervals and the difference eRR intervals. 
         FIG. 6  illustrates an exemplary flowchart for Ectopic Beat detection based on morphological impulse filtering of the RR intervals. 
         FIG. 7  shows the block diagram of applying a first morphological impulse filter to the input RR intervals to obtain the first filtered fRR intervals and the first difference eRR intervals, and then applying a second morphological filter to the first fRR intervals to obtain the second filtered ffRR intervals and the second difference eeRR intervals. 
         FIG. 8  illustrates an exemplary flowchart for NSVT detection based on morphological impulse filtering of the RR intervals. 
         FIG. 9  shows an exemplary segment of RR intervals that include ventricular Ectopic Beats and corresponding post-Ectopic Beat pauses. 
         FIG. 10  shows the original RR intervals, together with the filtered RR intervals after applying the opening operator. 
         FIG. 11  shows the original RR intervals, together with filtered RR intervals after applying the opening operator followed by the closing operator. 
         FIG. 12  shows the original RR intervals, together with the filtered RR intervals after applying the closing operator. 
         FIG. 13  shows the original RR intervals, together with filtered RR intervals after applying the closing operator followed by the opening operator. 
         FIG. 14  shows the original RR intervals, together with the filtered intervals fRR after applying the impulse filter illustrated in  FIG. 5 . 
         FIG. 15  shows the difference intervals between the original RR intervals and their corresponding filtered fRR intervals. 
         FIG. 16  shows the original RR intervals shown in  FIG. 9 , and the results of Ectopic Beat detection. 
         FIG. 17  shows another example of a long episode of RR intervals, together the filtered fRR intervals after applying the morphological filter shown in  FIG. 5 . 
         FIG. 18  shows the original RR intervals shown in  FIG. 17 , and with the results of Ectopic Beat detection. 
         FIG. 19  shows a zoomed view of  FIG. 17  that includes a segment of RR intervals and the corresponding filtered fRR intervals. 
         FIG. 20  shows a zoomed view of  FIG. 18  that includes a segment of RR intervals and the Ectopic Beat detection results. 
         FIG. 21  shows the initially filtered fRR intervals shown in  FIG. 19 , together with the further filtered intervals ffRR. 
         FIG. 22  shows the initially filtered fRR intervals, together with the NSVT detection results. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description is of the best mode presently contemplated for carrying out the invention. This description is not to be taken in a limiting sense, but is made merely for the purpose of describing the general principles of the invention. The scope of the invention should be determined with reference to the claims. 
     Embodiments of the invention provide a method for automatic detection of abnormal cardiac intervals, including but are not limited to Ectopic Beats, by means of cardiac inter-beat interval analysis. The cardiac inter-beat intervals are preferably the RR intervals that are measured from the surface ECG signals (e.g., by Holter monitoring), or from the subcutaneous ECG signals (e.g., by implantable subcutaneous ECG monitoring), or from the intracardiac electrogram (e.g., by implantable pacemakers or defibrillators). Alternatively, the cardiac inter-beat intervals can also be obtained from other types of biosignals that are known to show the same rhythmic variation as the cardiac beats, including but not limited to, the blood pressure signal, the transthoracic impedance signal, the pulse oximeter signal, finger plethysmography signal, etc. In the following descriptions, we use subcutaneous ECG as an example to illustrate the concept of morphological filtering of RR intervals for detection of abnormal cardiac intervals. 
       FIG. 1  shows a block diagram of an implantable device  10  for subcutaneous ECG monitoring, and its interfaces with an external programmer  12  and an external portable device  14 , which further communicates with the remote service center  16 . A similar apparatus, yet with different application for semi-automatic atrial defibrillation, has been described in U.S. Pat. Appl. No. US2007/0265667 filed by the present assignee. 
     Refer to  FIG. 1 . The implantable device  10  consists of an electronic circuitry that is hermetically sealed inside a Can, which is made from a biocompatible conductive material such as titanium, a non-conductive header attached to the Can, two or more sensing electrodes  18 , with or without leads connected to the header. 
     The sensing electrodes  18 , which are electrically isolated from one another, are mounted over the outer surface of the Can, or outside the header, or at the distal end of the leads (if available). For subcutaneous ECG recording, one or more pairs of sensing electrodes  18  form the sensing vectors and the inter-electrode distance is preferably greater than 3 cm. 
     The leads are optional for subcutaneous ECG recording. Generally, if the measured subcutaneous ECG amplitude is too small for reliable sensing, despite configuring different sensing vectors and recording at different anatomical locations, then one or more subcutaneous leads (with distal electrodes) could be tunneled under the patient&#39;s skin and connected to the header, so that larger subcutaneous ECG amplitude could be measured by increasing inter-electrode distance, e.g., between the lead electrode and the Can or header electrode. 
     Still refer to  FIG. 1 . Enclosed inside the hermetically sealed Can, a microprocessor  20  and associated circuitry make up the controller of the implant device  10 . The implant device  10  is powered by a battery  22 , and maintains an internal clock  24  for timing the operations. The microprocessor  20  communicates with a memory  26  via a bi-directional data bus. The memory  26  typically comprises a ROM or RAM for program storage and a RAM for data storage. 
     The sensing electrodes  18  are first connected to an electronic interface  28  that preferably includes a feedthrough circuitry for noise reduction, a high voltage protection circuitry, a switch network circuitry for sensing channel selection, and front-end analog filters, as well known in the field. The configurations of the interface circuitry  28  (e.g., filter settings, sensing channel selection, etc.) can be programmed by the microprocessor  20 . 
     The microprocessor  20  connects to an I/O control unit  30  to manage the input and output of the implant device  10 . One input signal is the subcutaneous ECG picked up by the sensing electrodes  18 . After pre-processed by the interface circuitry  28 , the subcutaneous ECG signal is further processed by the ECG sensing unit  32 , which usually consists of amplifiers, analog-to-digital converters, digital filters, etc., as known in the art. 
     Another input signal is the impedance (Z) signal measured between the sensing electrodes  18  by an impedance measurement unit  34 . By injecting a small constant current (e.g., 100 uA, preferably biphasic) between two electrodes  18  while measuring the voltage difference between the same or different pair of electrodes  18 , the impedance is calculated as the ratio between the measured voltage difference and the injecting current strength. As known in the art, the impedance signal provides useful information on the integrity of the sensing channel. In addition, the continuously measured impedance signal may be further processed by the microprocessor  20  to extract other physiological status of the patient, such as the respiration rate. 
     Other types of biological signals measured by specific sensors can also serve as input to the implant device  10 . For example, an on-board accelerometer can serve as a motion sensor  36  that provides patient&#39;s activity signal to the implant device  10 , an on-board (or embedded in the lead) temperature sensor  38  can provide the subcutaneous temperature signal to the implant device  10 . Other types of input signals include, but are not limited to, the subcutaneous pressure signal measured by a pressure sensor, the acoustic signal measured by an acoustic sensor, the subcutaneous pH signal measured by a pH sensor, etc. 
     By running the program stored in the memory  26 , the microprocessor  20  also sends instructions to the ECG sensing unit  32 , the impedance measurement unit  34 , and other input measurement units to control how these signals are acquired (e.g., gain, offset, filter settings, sampling frequency, sampling resolution, etc.). 
     The acquired biological signals are then stored in the device memory  26  and analyzed by the microprocessor  20  by running programmed algorithms. For example, the microprocessor  20  continuously analyze the acquired subcutaneous ECG signals to detect the peak of QRS complex. Such QRS peak detection can be achieved by many different means. In a preferred embodiment, the QRS peak detection is achieved by using an Auto-Sensing algorithm that automatically adjust the sensing threshold, which is adaptive to the measured peak amplitude of the QRS complex and varies based on a predetermined time dependence. One exemplary Auto-Sensing algorithm has been disclosed in U.S. Pat. No. 5,891,048, assigned to the present assignee. 
     Accordingly, the implant device  10  measures the intervals between any two adjacent peaks of the detected QRS complexes, and these intervals are termed RR intervals. These measured RR intervals are stored in the device memory  26  according to predefined storage modes. One typical mode is the queue-loop mode, meaning the measured RR intervals are stored in a predefined memory space, and while the allocated memory space is full, the newly measured RR intervals replace the oldest stored RR interval data. Another typical mode is the snapshot mode, meaning the measured RR intervals are stored in a predefined memory space, and while the allocated memory space is full, the newly measured RR intervals are not stored until the microprocessor  20  decides to store another episode of RR intervals. Yet another typical mode is the mixed mode, in which one or more segments of allocated memory space store the RR intervals in queue-loop mode, whereas one or more segments of separately allocated memory space store the RR intervals in snapshot mode. 
     Similarly, the microprocessor  20  can also continuously analyze the acquired subcutaneous ECG signals to measure other metrics of the QRS complex, such as the width of the QRS complex, the positive or negative peak amplitude of the QRS complex, the absolute area under the QRS complex, the maximum positive or negative slopes of the QRS complex, the dominant frequency component of the QRS complex, the complexity measures (e.g., sampled entropy) of the QRS complex, and so on. Likewise, the time series of these measured metrics are stored in the device memory  26  for further analysis. 
     The implant device  10  also includes a radio-frequency (RF) telemetry unit  40 . The RF telemetry unit  40  may be of the type well known in the art for conveying various information which it obtains from the implant device  10  to the external programmer  12 , or for receiving programming parameters from the external programmer  12  and then conveys to the implant device  10 . In one typical embodiment, the external programmer  12  can interrogate the implant device  10  to get the status of the implant device  10  (e.g., battery status, sensing channel impedance, etc.) or the data recorded by the implant device  10  (e.g., peak amplitude of the QRS complexes, statistics of measured RR intervals, etc.). In another typical embodiment, the external programmer  12  can be used to activate or deactivate selected algorithms or update programmable parameters of the implant device  10 . 
     In addition, the external portable device  14  to be described hereinafter, can also communicate bi-directionally with the implant device  10  through the telemetry unit  40 . Preferably, the data that may be received from or sent to the external portable device  14  are more limited as compared to the data that may be received from or sent to the external programmer  12 . 
     In a preferred embodiment, the data that are transmitted from the external portable device  14  to the implant device  10  are simple commands, such as trigger a snapshot of the acquired subcutaneous ECG, retrieve most recently diagnostic information from the implanted device  10 , etc. These commands set the implant device  10  into one of a number of modalities wherein each modality is determined and controlled by parameters that can only be selected by a physician operating the external programmer  12  using secure password or codes. 
     The data that are transmitted from the implant device  10  to the external portable device  14  preferably include simple acknowledgment to confirm receiving the commands from the external portable device  14 , the signals warning the detection of abnormal conditions, such as detection of atrial fibrillation (AF), detection of high ventricular rate (HVR), detection of low ventricular rate (LVR), detection of abnormal sensing impedance, detection of abnormal temperature, and so on. Other diagnostic information, such as the AF burden, the frequency of ectopic beats, snapshots of RR intervals or subcutaneous ECG, etc., can also be transmitted to the external portable device  14 . Preferably, a physician operating the external programmer  12  using secure password or codes controls the enable or disable condition as well as the amount of data that can be transmitted from the implant device  10  to the external portable device  14 . 
     Still refer to  FIG. 1 . The external portable device  14  has a power source  42 , such as a lithium battery, which provides power to the electrical components of the device  14 . The battery  42  is chargeable by connecting to an external charger  44 . The external portable device  14  also maintains an internal clock  46  for timing its operations. The overall functioning of the external portable device  14  is controlled by its microprocessor  48 , which reads and performs instructions stored in its associated memory  50 . The instructions stored in memory  50  preferably include instructions defining a communication protocol compatible with the implant device  10 , and instructions defining a communication protocol compatible with the remote service center  16 . 
     The microprocessor  48  of the external portal device  14  communicates with an I/O control unit  52  to read from the keypad  54  (or press switches) the patient input commands. In an exemplary embodiment, one subset of the input commands is designed to configure the external portable device  14 , for example, to turn on or off certain outputs  56  as described hereinafter, or select specific communication protocols. Another subset of the input commands is designed to establish communication between the external portable device  14  and the remote service center  16  through remote communication unit  58 . For example, patient&#39;s input  54  can command the external portable device  14  to transmit diagnostic information (retrieved from the implant device  10 ) to the remote service center  16 , and wait to receive acknowledgement. The third subset of the commands is designed to establish communication between the external portable device  14  and the implant device  10  through implant communication unit  60 . For example, patient&#39;s input  54  can command the external portable device  14  to transmit corresponding signals to the implant device  10  to trigger recording a snapshot of the subcutaneous ECG, to retrieve diagnostic information from the implanted device  10 , etc. The implant communication unit  60  also receives the acknowledgement and related diagnostic information sent from the implant device  10 , and conveys these data to the microprocessor  48  for storage in the memory  50 . 
     According to one exemplary embodiment of the present invention, upon receiving a predefined warning signal from the implant device  10  (e.g., detection of AF, detection of HVR, detection of LVR, detection of abnormal sensing impedance, detection of abnormal temperature, etc.), the microprocessor  48  of the external portable device  14  communicates with the I/O control unit  52  to generate output  56  that is perceptible by the patient. Such output  56  can be in the form of visible message, such as the light-up or blinking of a light emitting diode (LED) or the text message displayed in a liquid crystal display (LCD), or in the form of audible message such as beep, ringing tone, or pre-recorded voice messages played by a speaker, or in the form of discernible vibration by a vibrator. According to the patient&#39;s preference, one or multiple types of warning message can be respectively turned on or off. For example, the visible warning message can be turned on while the audible warning message can be turned off during the night if the patient chooses not to be disturbed during sleep even if the implant device  10  detects AF. Besides generating warning messages, some diagnostic information that is received from the implant device  10  and stored in memory  50  (e.g., the heart rate) can also be provided to the patient in the form of visual or audible messages. 
     The external portable device  14 , via its remote communication unit  58 , can further communicate with the remote service center  16 . Such long-range communication apparatus can be in the form of a mobile radio network, or a fixed-line telecommunication network, or the internet, as well known in the art. Examples of such long-range communication apparatus have been taught in U.S. Pat. No. 6,470,215, U.S. Pat. No. 6,574,509, U.S. Pat. No. 6,622,043, all are assigned to the assignee of the present invention and incorporated herein by reference. 
     In one typical embodiment, the external portable device  14  transmits the implant device status information (e.g., battery status, sensing impedance, etc.) as well as relevant diagnostic information (e.g., AF burden, Ectopic Beat frequency, etc.) to the remote service center  16  according to a predefined transmission frequency and schedule (e.g., every midnight, etc.). Yet in another typical embodiment, the external portable device  14  communicates with the remote service center  16  in a trigger mode, for example, upon receiving a warning signal from the implant device  10 , or upon the patient trigger. In such cases, the external portable device  14  transmits critical diagnostic information stored in memory  50  (e.g., AF burden, mean heart rate, the subcutaneous ECG snapshot, etc.) to the remote service center  16 . 
     The remote service center  16  receives the information via compatible communication protocols, then sends acknowledgement back to the external portable device  14 , which may generate visible or audible output  56  indicating receipt of the acknowledgement. The data received by the remote service center  16  is stored in central database, and is promptly presented to the patient&#39;s physician or responsible expert through proper means, such as fax or email as known in the art. By reviewing the received diagnostic information, the physician can evaluate the patient&#39;s condition and provide expert advice to the patient who wishes to contact the physician in response to the warning signals generated by the external portable device  14 . 
     The method to detect abnormal cardiac intervals using morphological operators is disclosed hereinafter. 
     According to a preferred embodiment of this invention, the implant device  10  continuously senses the subcutaneous ECG signals, detects the peak of QRS complex, and measures the RR intervals. The device  10  also maintains a first-in-first-out (FIFO) running buffer that stores the measured RR intervals of the most recent L cardiac cycles, where L is a predefined parameter that can be programmed through the external programming device. 
     According to another embodiment of this invention, the device  10  continuously senses the subcutaneous ECG signals, detects the peak of QRS complex, and derives one or more metrics from the QRS complex. These metrics include but are not limited to, the width of the QRS complex, the positive or negative peak amplitude of the QRS complex, the absolute area under the QRS complex, the maximum positive or negative slopes of the QRS complex, the dominant frequency component of the QRS complex, the complexity measures (e.g., sampled entropy) of the QRS complex, and so on. Similarly, the device  10  maintains a FIFO running buffer that stores the derived metrics of the most recent L cardiac cycles, where L is a predefined parameter that can be programmed through the external programming device. 
     Also according to this invention, morphological operators are implemented, either in embedded software or in hardware platform of the device  10 . As described in details later, these morphological operators are applied to the measured RR intervals, to remove the abnormal RR intervals to get the trend of RR intervals, and detect the abnormal RR intervals, including but are not limited to Ectopic Beats, episode of NSVT, sudden RR pauses, etc. 
     According to this invention, the device  10  calculates and maintains a plural of statistics based on the detected abnormal RR intervals, including but are not limited to, the Ectopic Beat counter, the Ectopic Beat frequency, etc. In addition, the filtered RR intervals (without abnormal RR intervals) are used to calculate the conventional HRV parameters, such as the SDANN, pNN50, as well known in the art. The baseline heart rate, circadian variation of the heart rate, and the heart rate trend are also measured based on the filtered RR intervals (i.e., free of abnormal RR intervals). Furthermore, the HRT after Ectopic Beat can also be calculated and logged by the device  10 . 
     Now the concept of morphological operators is described. There are two basic morphological operators: erosion and dilation. These basic operators are usually applied in sequence that yields two derived morphological operations: opening and closing. 
     Denote F=[f 0 , f 1 , . . . , f N-1 ] the discrete input signal, and denote K=[k 0 , k 1 , . . . , k M-1 ,] a predefined discrete kernel function, also called structure element (SE), where N and M are two integers that N&gt;M. 
     The erosion of the signal F by the structure element K, denoted FΘK, is defined as: 
                     F   ⁢           ⁢   Θ   ⁢           ⁢     K   ⁡     (   i   )         =         min       j   =   0     ,           ⁢       …   ⁢           ⁢   M     -   1         ⁢     f     i   +   j         -     k   j                   for   ⁢           ⁢   i     =   0     ,   1   ,   …   ⁢           ,     N   -   M                 
The erosion is a shrinking operation in that values of FΘK are always less than those of F if all elements of the SE are greater than zero.  FIG. 2A  shows the circuit block diagram of implementing the erosion operator. The input signal passes through a cascade of delay units  100 ,  100 ′ and  100 ″. The structuring elements  102 ,  102 ′ and  102 ″ and  102 ′″ are subtracted from the input samples with corresponding delay taps  104 ,  104 ′,  104 ″ and  104 ′″. For each snapshot of the input signal with segment length M, one output sample is generated, by finding the minimum  106  of the subtracted values. Note that compared to the input signal, the erosion output is delayed by M−1 taps. Also note that if SE is an all zero vector, than the subtraction operation is not needed.
 
     The dilation of the signal F by the structure element K, denoted F{circle around (+)}K, is defined as: 
                     F   ⊕     K   ⁡     (   i   )         =         max       j   =     i   -   M   +   1       ,           ⁢   …   ⁢           ,   i       ⁢     f   j       +     k     i   -   j                     for   ⁢           ⁢   i     =     M   -   1       ,   M   ,   …   ⁢           ,     N   -   1                 
The dilation is an expansion operation in that values of F{circle around (+)} K are always larger than those of F if all elements of the SE are greater than zero.  FIG. 2B  shows the circuit block diagram of implementing the dilation operator. The input signal passes through a cascade of delay units  150 ,  150 ′ and  150 ″. The structuring elements  152 ,  152 ′ and  152 ″ and  152 ′″ are reversed and then added to the input samples with corresponding delay taps  154 ,  154 ′,  154 ″ and  154 ′″. For each snapshot of the input signal with segment length M, one output sample is generated, by finding the maximum  156  of the added values. Note that compared to the input signal, the dilation output has no time delay. Also note that if SE is an all zero vector, than the addition operation is not needed.
 
     As illustrated in  FIG. 3A , opening of a data sequence by a SE is defined as erosion  202  followed by a dilation  204 . The opening of a data sequence can be interpreted as sliding the SE along the data sequence from beneath and the result is the highest points reached by any part of the SE. As further illustrated in  FIG. 3B , closing of a data sequence by a SE is defined as dilation  204 ′ followed by an erosion  202 ′. The closing of a data sequence can be interpreted as sliding a ‘flipped-over’ version of the SE along the data sequence from above and the result is the lowest points reached by any part of the SE. 
     In typical applications, opening is used to suppress peaks while closing is used to suppress pits. Therefore, in order to suppress both peaks and pits, opening and closing are usually used in pairs. For example,  FIG. 4A  shows the block diagram of an impulse filter that removes both peaks and pits by applying an opening operation  302  followed by a closing operation  304 . Similarly,  FIG. 4B  shows the block diagram of another impulse filter by applying a closing operation  304 ′ followed by an opening operation  302 ′.  FIG. 4C  shows yet another block diagram of an impulse filter that combines the previous two filters. In this case, the opening-closing pair ( 302 ″ and  304 ″) and the closing-opening pair ( 304 ′″ and  302 ′″) operate in parallel, and their outputs are averaged ( 306 ) to generate the filtered output. 
     The design of the SE depends on the shape of the signal that is to be preserved. Since the opening and closing operations are intended to remove impulses, the SE must be designed so that the trend of the RR intervals is preserved. A SE is characterized by its shape, width, and height. It has been demonstrated that the width of the SE plays a more important role, compared to either the height or the shape, in determining the impulse suppression performance. In the following description of the embodiments of the present invention, the SE is considered as an all zero vector with predefined width, although it is obvious that other types of SE can be defined. 
     Now the method to detect abnormal cardiac intervals based on device stored RR intervals is disclosed. Because the abrupt increase or decrease of the abnormal cardiac intervals are usually characterized by positive or negative impulses in the tachogram (termed as impulse RR intervals in the following description), the morphological operators are particularly suitable for detecting these RR interval spikes. 
     Now refer to  FIG. 5 . According to this invention, the time series of RR intervals provide input to an impulse filter  310  that forms a second signal analyzer comprising two branches in parallel, opening-closing ( 312  and  314 ) and closing-opening ( 314 ′ and  312 ′), and their results are averaged ( 316 ) to get filtered RR intervals fRR. As indicated in  FIG. 4 , either branch of the impulse filter can be used alone to obtain fRR. The RR is subtracted from fRR ( 318 ) to get their difference intervals eRR. 
     In a typical embodiment, the structure elements used in all opening and closing operations of the impulse filter  310  shown  FIG. 5  are identical. For removal of isolated RR spikes (e.g., ectopic beats, post-Ectopic Beat pauses, etc.), the SE width is preferably short. For example, the SE for the RR impulse filter is preferably defined as a three-zero vector [0, 0, 0]. Also according to the present invention, the SE width for the RR impulse filter is user-programmable or selectable from a predetermined range, e.g., from 3 to 5. 
     By applying the morphological impulse filter to the RR intervals, the abrupt lengthening and abrupt shortening of the RR intervals (or impulse RR intervals) are removed. Thus the output fRR intervals preserve the trend of the RR intervals without any impulse RR intervals. On the other hand, the eRR intervals quantify the deviation of each RR interval from the corresponding trend interval (fRR). Thus for normal cardiac cycles, eRR intervals are close to zero, whereas for impulse RR intervals, eRR intervals have large (positive or negative) values. 
     Now refer to  FIG. 6 , which illustrates an exemplary flowchart for Ectopic Beat detection. Using the morphological impulse filter  400  that is described in  FIG. 5 , the input RR intervals  402  are continuously processed to generate the corresponding filtered fRR intervals  404 . For each input RR interval  402  and the corresponding filtered output fRR interval  404 , their ratio r 1 =RR/fRR ( 406 ) is calculated. In addition, the deviation between input RR interval  402  and the corresponding filtered output fRR interval  404  is calculated as eRR=fRR−RR ( 408 ). The corresponding heart rate difference is also calculated as eHR=60/RR−60/fRR ( 408 ) (assuming eHR unit is bpm, and RR and fRR units are seconds). In one typical embodiment, an Ectopic Beat is detected ( 410 ) if two conditions are met: (1) r 1  is below a first predefined threshold that is less than 1.0 ( 412 ) (e.g., 0.90), and (2) eRR is greater than a second predefined threshold ( 414 ) (e.g., 50 ms). In another embodiment, the second condition is replaced by requiring eHR is greater than a third predefined threshold ( 414 ′) (e.g., 5 bpm). Yet in another embodiment, the second condition is changed to requiring either eRR is greater than a second predefined threshold ( 414 ) (e.g., 50 ms), or eHR is greater than a third predefined threshold ( 414 ′) (e.g., 5 bpm), as shown in the figure. Alternatively, the second condition may be changed to requiring both eRR is greater than a second predefined threshold ( 414 ) (e.g., 50 ms), and eHR is greater than a third predefined threshold ( 414 ′) (e.g., 5 bpm). 
     Now refer to  FIG. 7 . According to this invention, the initially filtered fRR intervals (free of impulse RR intervals) are further processed by a second morphological impulse filter  500  to detect multiple consecutive abnormal RR intervals. Similarly, this second morphological filter consists of two branches in parallel, opening-closing ( 512  and  514 ) and closing-opening ( 514 ′ and  512 ′), and their results are averaged ( 516 ) to get further filtered intervals termed ffRR. As indicated in  FIG. 4 , either branch of the impulse filter can be used alone to obtain ffRR. The fRR is subtracted from ffRR ( 518 ) to get their difference intervals eeRR. 
     In a typical embodiment, the structure elements used in all opening and closing operations of the second impulse filter  500  shown  FIG. 7  are identical. For removal of multiple consecutive abnormal cardiac intervals (e.g., NSVT episode, or a short run of ventricular pauses), the SE width is preferably longer than the maximum count of consecutive abnormal cardiac cycles. For example, the SE for the RR impulse filter is preferably defined as an 11-zero vector [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. Also according to the present invention, the SE width for the RR impulse filter is user-programmable or selectable from a predetermined range, e.g., from 7 to 15. 
     By applying the second morphological filter to the fRR intervals, the multiple consecutive abnormal RR intervals are removed. Thus the output ffRR intervals preserve the trend of the RR intervals without any impulse RR intervals, or any short runs of brady/tachy RR intervals. On the other hand, the eeRR intervals quantify the deviation of each fRR interval from the corresponding trend interval (ffRR). Thus for normal cardiac cycles or impulse RR intervals, eeRR intervals are close to zero, whereas for multiple consecutive abnormal cardiac intervals, eeRR intervals have large (positive or negative) values. 
     Now refer to  FIG. 8 , which illustrates an exemplary flowchart for NSVT detection. Using the morphological filters  600  that are described in  FIG. 7 , the input RR intervals  602  are continuously processed to generate the first filtered output fRR intervals and the second filtered output ffRR intervals  604 . For each fRR interval and the corresponding ffRR interval, their ratio r 2 =fRR/ffRR is calculated ( 606 ). In addition, the deviation between fRR interval and the corresponding ffRR interval is calculated as eeRR=ffRR−fRR ( 608 ). The corresponding heart rate difference is also calculated as eeHR=60/fRR−60/ffRR ( 608 ) (assuming eeHR unit is bpm, and fRR and ffRR units are seconds). In one typical embodiment, an episode of NSVT is detected ( 610 ) if two conditions are met: (1) r 2  is below a first predefined threshold that is less than 1.0 ( 612 ) (e.g., 0.90), and (2) eeRR is greater than a second predefined threshold ( 614 ) (e.g., 50 ms). In another embodiment, the second condition is replaced by requiring eeHR is greater than a third predefined threshold ( 614 ′) (e.g., 5 bpm). Yet in another embodiment, the second condition is changed to requiring either eeRR is greater than a second predefined threshold ( 614 ) (e.g., 50 ms), or eeHR is greater than a third predefined threshold ( 614 ′) (e.g., 5 bpm), as shown in the figure. Alternatively, the second condition may be changed to requiring both eeRR is greater than a second predefined threshold ( 614 ) (e.g., 50 ms), and eeHR is greater than a third predefined threshold ( 614 ′) (e.g., 5 bpm). 
       FIGS. 9-22  show some examples of applying morphological filters to RR intervals. 
       FIG. 9  shows an exemplary tachogram of 40 RR intervals that include 5 ventricular Ectopic Beats and 5 corresponding post-Ectopic Beat pauses. 
       FIG. 10  shows the original 40 RR intervals, together with the filtered RR intervals after applying the opening operator (with a three-zero SE in this example). Clearly, the positive RR peaks are removed after the opening operation.  FIG. 11  shows the original 40 RR intervals, together with filtered RR intervals after applying first the opening operator (with a three-zero SE in this example) then by the second closing operator (with a three-zero SE in this example). Clearly, both positive RR peaks and negative RR pits are removed after the opening-closing operations. 
       FIG. 12  shows the original 40 RR intervals, together with the filtered RR intervals after applying the closing operator (with a three-zero SE in this example). Clearly, the negative RR pits are removed after the closing operation.  FIG. 13  shows the original 40 RR intervals, together with filtered RR intervals after applying first the closing operator (with a three-zero SE in this example) then by the second opening operator (with a three-zero SE in this example). Clearly, both positive RR peaks and negative RR pits are removed after the opening-closing operations. 
       FIG. 14  shows the original 40 RR intervals, together with the filtered intervals fRR after applying the impulse filter illustrated in  FIG. 5 , where the impulse filter consists of two branches in parallel, opening-closing and closing-opening, and their results are averaged to get the filtered intervals fRR. Clearly, both positive RR peaks and negative RR pits are filtered out in fRR intervals. Compared to  FIG. 11  and  FIG. 13 , the filtered output fRR (averaged results from two parallel branches) is less biased and better preserves the RR trend information. 
       FIG. 15  shows the difference intervals RR-FRR, that is, the difference between the original 40 RR intervals and their corresponding filtered intervals fRR (note the difference intervals plotted in this figure is the negative of eRR=fRR−RR). The ventricular Ectopic Beats are clearly identified by the 5 negative spikes of the difference intervals, whereas the post-Ectopic Beat pauses are clearly identified by the 5 positive spikes of the difference intervals. 
       FIG. 16  shows the original 40 RR intervals and the results of Ectopic Beat detection (ventricular Ectopic Beats are marked by circles), by applying the morphological filters to the RR intervals, and using the detection criteria shown in  FIG. 6 . 
       FIG. 17  shows another example of a long episode of tachogram that consists of over 8000 RR intervals, as well as the filtered RR intervals (fRR) after applying the morphological filter shown in  FIG. 5  to remove the impulse RR intervals. The original RR intervals are further plotted in  FIG. 18 , together with the results of Ectopic Beat detection (ventricular Ectopic Beats are marked by circles), after applying the morphological filters to the RR intervals, and using the detection criteria shown in  FIG. 6 . 
       FIG. 19  shows a zoomed view of  FIG. 17  that includes a segment of 300 RR intervals and the corresponding filtered fRR intervals. Note that there are 7 consecutive short RR intervals that are not removed by the impulse filter.  FIG. 20  shows a zoomed view of  FIG. 18  that includes the same segment of 300 RR intervals and the Ectopic Beat detection results. Evidently, these 7 consecutive short RR intervals are not detected as ventricular Ectopic Beats. 
     As illustrated in  FIG. 7 , the initially filtered fRR intervals (free of impulse RR intervals) can be further processed by a second morphological impulse filter to detect multiple consecutive abnormal RR intervals.  FIG. 21  shows the initially filtered  300  fRR intervals shown in  FIG. 19 , together with the further filtered intervals ffRR (note the second morphological impulse filter uses an 11-zero SE in this example). Clearly, the 7 consecutive short RR intervals are removed in the ffRR intervals. The initially filtered  300  fRR intervals are further plotted in  FIG. 22 , together with the NSVT detection results (marked by circles) using the detection criteria shown in  FIG. 8 . Clearly, the 7 consecutive short RR intervals are corrected detected as an episode of NSVT. 
     As illustrated in the above examples, using morphological filters can effectively remove the impulse RR intervals to get the trend of RR intervals, and detect the abnormal cardiac intervals, including but are not limited to Ectopic Beats, episode of NSVT, sudden RR pauses, etc. These morphological operators are particularly suitable for application in low-power devices such as the subcutaneous ECG monitor as exemplified in this invention, because of their very high computation efficiency, and feasibility for implementation in hardware platform. 
     While the above descriptions use subcutaneous ECG as an example to illustrate the concept of morphological filtering of RR intervals, it is also obvious to the people who are skilled in the art to apply the same concept and method to general time series analysis of cardiac intervals, e.g., to detect and remove abnormal cardiac beats based on RR intervals measured from a plural of biological signals, including but are not limited to, the surface ECG signal, the IEGM signal, the blood pressure signal, the transthoracic impedance signal, the pulse oximeter signal, finger plethysmography signal, etc. 
     Furthermore, it is also obvious to the people skilled in the art to apply the same concept and method for abnormal beat detection and removal based on time series analysis of various metrics (other than the RR intervals) that are derived from the biological signal, such as surface ECG, subcutaneous ECG, IEGM, blood pressure, etc. For instance, it is well known that the QRS morphology of a ventricular Ectopic Beat is usually different than that of a normal conducted beat. Such morphological difference can be quantified by means of a plural of metrics, including but are not limited to, the width of the QRS complex, the positive or negative peak amplitude of the QRS complex, the absolute area under the QRS complex, the maximum positive or negative slopes of the QRS complex, the dominant frequency component of the QRS complex, the complexity measures (e.g., sampled entropy) of the QRS complex, and so on. Therefore, abnormal beat detection and removal can also be achieved by applying the morphological filters to the time series of these derived metrics. 
     It is further understood that abnormal beat detection and removal can also be achieved by applying morphological filters independently to two or more physiological signals. For example, one set of morphological filters are applied to the measured RR intervals, and another set of morphological filters are applied to the time series of a derived metric (e.g., QRS width). The morphological filtering of multiple physiological signals run in parallel, and each branch performs independent impulse (RR interval or derived metric) detection and removal. The results from these multiple branches are then pooled together for final detection and removal of the abnormal cardiac cycles. In one typical embodiment, a beat is classified as an abnormal beat if all branches of the morphological filtering classify the beat as an abnormal beat. In another typical embodiment, a beat is classified as an abnormal beat if any branch of the morphological filtering classifies the beat as an abnormal beat. Obviously other logical operations can be similarly implemented. 
     Although an exemplary embodiment of the present invention has been shown and described, it should be apparent to those of ordinary skill that a number of changes and modifications to the invention may be made without departing from the spirit and scope of the invention. All such changes, modifications and alterations should therefore be recognized as falling within the scope of the present invention.