Patent Publication Number: US-8126539-B2

Title: Method and apparatus for monitoring T-wave alternans

Description:
TECHNICAL FIELD 
     The invention relates generally to medical devices and, in particular, to a device and method for monitoring T-wave alternans in a patient. 
     BACKGROUND 
     T-wave alternans is beat-to-beat alternation in the morphology, amplitude, and/or polarity of the T-wave, and can be observed on surface electrocardiogram (ECG) recordings. T-wave alternans (TWA) has been recognized in a variety of clinical conditions, including acquired and congenital long QT syndrome and ischemic heart disease associated with ventricular arrhythmias. TWA is considered an independent predictor for cardiac arrhythmias. Experimentally, TWA has been shown to be a precursor of ventricular tachycardia. 
     In past practice, TWA has been assessed from surface ECG recordings obtained in a clinical setting as an indication of the long-term risk for ventricular arrhythmias. A Fast Fourier Transform (FFT) method is used for frequency domain analysis of T-waves, and a Modified Moving Averaging (MMA) method is used for time domain analysis of T-waves. The low-amplitude changes in the T-wave signal during TWA, which is on the order of microvolts, requires complicated software to assess TWA from a surface ECG recording of typically 128 heart beats or more during exercise or high-rate atrial pacing. Such methods require storing and analyzing more cardiac cycles than are typically available in an implantable cardioverter defibrillator (ICD). TWA assessment using fewer cardiac cycles for practical implementation in ICDs for use in short-term prediction of the onset of arrhythmias is needed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of an IMD system that may be used for monitoring TWA. 
         FIG. 2  illustrates one IMD configuration for acquiring EGM data in a TWA assessment method. 
         FIG. 3  is a frontal and plan view of a subcutaneous implantable cardioverter defibrillator which may be used for acquiring ECG data in a TWA assessment method. 
         FIG. 4  is a flow chart summarizing a method for assessing TWA according to one embodiment of the invention. 
         FIG. 5A  is a diagram of superimposed consecutive T-wave signals illustrating the presence of TWA. 
         FIG. 5B  is a diagram of superimposed consecutive T-wave signals acquired during TWA using narrow band filtering, e.g. 3 to 100 Hz filtering, as might be used in ICDs. 
         FIG. 6A  is a diagram of a pair of T-wave signals illustrating one method for evaluating T-wave signal pairs for detecting TWA in an implantable device. 
         FIG. 6B  is a diagram of a pair of T-wave signals illustrating an alternative method for evaluating T-wave signal pairs for detecting TWA in an implantable device. 
         FIG. 7  is an EGM signal indicating sliding signal analysis windows encompassing a selected number of T-wave signal pairs. 
         FIG. 8  is a flow chart of a method for determining a TWA metric for detecting the presence of TWA. 
         FIG. 9A  is a graph of simulation results of TWA measurements performed using FFT and MMA methods compared to using a two-point amplitude assessment as described herein. 
         FIG. 9B  is a graph of simulated TWA measurements during noise input using FFT, MMA and a two point analysis method as described herein. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, references are made to illustrative embodiments for carrying out the invention. It is understood that other embodiments may be utilized without departing from the scope of the invention. For purposes of clarity, the same reference numbers are used in the drawings to identify similar elements. As used herein, the term “module” refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, or other suitable components that provide the described functionality. 
       FIG. 1  is a functional block diagram of an IMD system that may be used for monitoring TWA. The system provides for dynamic monitoring of TWA in an ambulatory patient. The system includes IMD  10  and associated electrodes  12  for acquiring EGM signals. EGM signals are used by IMD  10  for assessing the cardiac rhythm for determining if and when a therapy is needed. IMD  10  further uses the acquired EGM signals for TWA assessment as will be described herein. 
     IMD  10  may also be coupled to one or more physiological sensors  13 , such as an activity sensor or hemodynamic sensors, such as blood pressure sensors. Physiological signals may be used for detecting cardiac events such as arrhythmias or hemodynamic events. Physiological signals may be used by IMD  10  for triggering certain device operations. In one embodiment, physiological signals are used to trigger a TWA assessment. 
     IMD  10  is adapted for bidirectional communication with an external programmer/monitor  14  via telemetry circuitry  28 . Programmer/monitor  14  is used for programming operating parameters in IMD  10  and for uplinking data from IMD  10 . In accordance with one embodiment of the present invention, programmer/monitor  14  may be used by a clinician to initiate a TWA assessment. Alternatively, programmer/monitor  14  may be used to program parameters controlling an automated TWA assessment performed by IMD  10 . A TWA report may be received by programmer/monitor  14  from IMD  10  including TWA data and/or TWA assessment results. In some embodiments, EGM data acquired by IMD  10  for use in TWA assessment may be transferred to programmer/monitor  14  for analysis by programmer/monitor  14  or another external computer system such as a remote patient management network. IMD  10  may also be adapted for communicating with a patient activator  16  which may be used by a patient or other caregiver to initiate a TWA assessment. 
     IMD  10  includes an R-wave detector  30 , which receives EGM signals from electrodes  12  via switch matrix  11 . R-wave detector  30  includes a sense amplifier having frequency response characteristics and beat-by-beat automatic adjusting sensitivity for accurate R-wave detection. R-wave detection may generally correspond to that disclosed in U.S. Pat. No. 5,117,824 issued to Keimel et al., U.S. Pat. No. 6,393,316 issued to Gilberg et al., or U.S. Pat. No. 5,312,441 issued to Mader, et al., all of which patents are incorporated herein by reference in their entirety. 
     IMD  10  further includes an EGM sense amplifier  32  that may be used for acquiring EGM signals for specialized signal analyses. EGM sense amplifier  32  receives signals from electrodes  12  via switch matrix  11 . EGM sense amplifier  32  provides a wider band of frequency response than R-wave detector  30  and a separately adjustable gain setting. EGM sense amplifier  32  may be embodied as an automatic gain control sense amplifier enabled for automatic gain adjustment responsive to the amplitude of sensed T-wave signals. EGM signal segments for use in specialized analyses, such as TWA assessment, may be extracted from EGM signals obtained by sense amplifier  32  based on relative timing from R-waves detected by R-wave detector  30 . In particular, T-wave signal analysis is performed to obtain T-wave measurements during a T-wave sensing window selected relative to an R-wave detection signal from R-wave detector  30 . 
     Electrodes  12  may be located on leads extending from IMD  10  or may be leadless electrodes incorporated in or on the housing of IMD  10 . R-wave detector  30  and EGM sense amplifier  32  receive signals from electrodes  12  via switch matrix  11 . Switch matrix  11 , under the control of microprocessor  22 , is used for selecting which electrodes are coupled to R-wave detector  30  for reliable R-wave detection and which electrodes are coupled to EGM sense amplifier  32  for use in TWA assessment. 
     IMD  10  includes a signal conditioning module  18  for receiving EGM signals from EGM sense amplifier  32  and physiological signals from sensors  13 . Signal conditioning module  18  includes sense amplifiers and may include other signal conditioning circuitry such as filters and an analog-to-digital converter. Microprocessor  22  receives signals on system bus  21  from signal conditioning module  18  for detecting physiological events. 
     Memory  20  is provided for storing conditioned EGM signal output from conditioning module  18 . In one embodiment, processing of EGM signals for assessing TWA is performed by IMD microprocessor  22 . Microprocessor  22 , controls IMD functions according to algorithms and operating parameters stored in memory  20 . Microprocessor  22  may perform TWA assessment according to the methods to be described below. In response to TWA assessment results, microprocessor  22  may cause an alert signal to be generated by alarm circuitry  24 . Additionally or alternatively, a therapy delivery module  26  may be signaled to deliver or withhold a therapy, or adjust therapy delivery parameters under the control of timing and control circuitry  25 . In various embodiments, control circuitry implemented for performing automated TWA assessment in IMD  10  may include application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, or other suitable components that provide the described functionality. The implementation of TWA assessment provided herein is not limited to a particular type of system architecture. 
     In other embodiments, EGM data acquired by IMD  10  for use in TWA assessment may be stored in memory  20  and downlinked to external programmer/monitor  14 . All T-wave signal sample points may be stored for use in assessing TWA, or only specified data points may be stored, such as every 30 msec during the T-wave sensing window, for example, if memory space is limited. Processing circuitry included in programmer/monitor  14  may then perform a TWA assessment according to programmed-in algorithms. Reports of TWA assessment results may be generated by either IMD  10  or external programmer/monitor  14 , for display, printing or electronic storage such that the results are available for review by a clinician. 
       FIG. 2  illustrates one IMD configuration for acquiring EGM data in a TWA assessment method. IMD  10  may be embodied as any of a number of IMDs, such as a cardiac monitoring device, a pacemaker, an implantable cardioverter defibrillator, a neurostimulator, or a drug delivery device. EGM data suitable for assessing TWA may be acquired from signals sensed by subcutaneous electrodes, epicardial electrodes, transvenous or endocardial electrodes, or a neurostimulation lead. In one embodiment, multiple sensing vectors are selected for acquiring EGM data for TWA assessment. Multiple sensing vectors may be selected from any combination of available electrodes. 
     In the example shown in  FIG. 2 , IMD  10  is embodied as an implantable cardioverter defibrillator and is shown coupled to a set of leads adapted for delivering pacing, cardioversion, and defibrillation pulses and sensing EGM signals for detecting and discriminating heart rhythms. IMD  10  is coupled to a right ventricular (RV) lead  40  carrying a superior vena cava (SVC) coil electrode  46  and an RV coil electrode  48  for use in delivering cardioversion and defibrillation shock pulses. RV lead  40  carries a tip electrode  52  and a ring electrode  50  used in pacing and sensing functions in the right ventricle. 
     IMD  10  is further coupled to a coronary sinus (CS) lead  42  equipped with a tip electrode  56  and ring electrode  54  for use in sensing and pacing functions in the left heart chambers. CS lead  42  may be advanced into a cardiac vein so as to position CS tip electrode  56  and ring electrode  54  in a desired location over the left ventricle. 
     IMD  10  is provided with a can or case electrode  60  that may be used in combination with any of the cardiac electrodes for delivering stimulation pulses or sensing cardiac electrical signals in a unipolar mode. IMD  10  may be coupled to one or more subcutaneous leads  44  carrying a subcutaneous electrode  58 , which may be a coil, patch or other type of electrode used in combination with SVC coil electrode  46 , RV coil electrode  48 , and/or can electrode  60  for delivering cardioversion or defibrillation shock pulses. Subcutaneous electrode  58  may alternatively be used in combination with any of the tip or ring electrodes  50 ,  52 ,  54  and  56  for sensing or pacing in unipolar modes. 
     Numerous sensing vectors may be selected from the electrodes available in the system shown in  FIG. 2 . Any electrode located on RV lead  40  or CS lead  42  may be selected in a unipolar sensing combination with can electrode  60  or subcutaneous electrode  58 . Any combination of two electrodes located on RV lead  40  or CS lead  42  may be selected for bipolar sensing. Thus multi-vector sensing for TWA assessment may be achieved by selecting multiple unipolar and/or bipolar sensing electrode pairs, either simultaneously or sequentially, for collecting EGM signals. Both far-field and near-field EGM signals can be collected for TWA assessment. Multi-vector TWA analysis allows discrimination of concordant and discordant forms of TWA. The invention is not limited to the lead and electrode arrangement shown in  FIG. 2 . Numerous variations exist in the types of leads and electrodes that may be included in a system for monitoring TWA. 
       FIG. 3  is a frontal and plan view of a subcutaneous implantable cardioverter defibrillator (SubQ ICD)  114 . The functionality represented by the block diagram of  FIG. 1  may be implemented in a device coupled only to subcutaneously implanted electrodes, such as SubQ ICD  114 . TWA assessment methods described herein may rely on subcutaneous ECG sensing rather than intracardiac EGM sensing. SubQ ICD  114  includes a housing with a connector  125  for attaching a subcutaneous sensing and cardioversion/defibrillation therapy delivery lead  118 . 
     Subcutaneous lead  118  includes a distal defibrillation coil electrode  124 , a distal sensing electrode  126 , an insulated flexible lead body and a proximal connector pin  127  for connection to the housing of SubQ ICD  114  via connector  125 . SubQ ICD  114  further includes a subcutaneous electrode array (SEA)  128  welded into place on the flattened periphery of the housing of SubQ ICD  114 . The SEA  128  is welded to SubQ ICD housing (to preserve hermaticity) and is connected via wires to electronic circuitry inside SubQ ICD  114 . The SEA  128  shown in  FIG. 3  includes three electrodes positioned to form orthogonal signal vectors. Any of the electrodes included in SEA  128  or on subcutaneous lead  118  may be selected in any combination for sensing subcutaneous ECG signals for use in TWA assessment. The methods described herein for assessing TWA hereinafter refer to the use of EGM signals, however, it is recognized that subcutaneous ECG signals may be substituted for EGM signals. 
       FIG. 4  is a flow chart summarizing a method for assessing TWA according to one embodiment of the invention. Flow chart  200  is intended to illustrate the functional operation of an IMD system, and should not be construed as reflective of a specific form of software or hardware necessary to practice the invention. It is believed that the particular form of software will be determined primarily by the particular system architecture employed in the device and by the particular detection and therapy delivery methodologies employed by the device. Providing software to accomplish the present invention in the context of any modern IMD, given the disclosure herein, is within the abilities of one of skill in the art. 
     Methods described in conjunction with flow charts presented herein may be implemented in a computer-readable medium that includes instructions for causing a programmable processor to carry out the methods described. A “computer-readable medium” includes but is not limited to any volatile or non-volatile media, such as a RAM, ROM, CD-ROM, NVRAM, EEPROM, flash memory, and the like. The instructions may be implemented as one or more software modules, which may be executed by themselves or in combination with other software. 
     Method  200  for assessing TWA includes sensing an EGM signal at block  205 . T-wave signals are acquired by the IMD subsequent to the sensed R-waves at block  210 . Typically, a T-wave sensing window is set relative to detected R-waves for acquiring and storing a plurality of consecutive T-wave signals, for example 10 to 20 T-wave signals. The current heart rate (HR) may be recorded at block  210  for use in determining a TWA metric. Generally, if a TWA episode occurs, it will occur during an elevated heart rate. Depending on a disease state or other physiological conditions, a longer TWA episode or an episode having greater differences between alternating T-waves may occur at relatively lower heart rates. As such, the HR corresponding to a TWA episode may be used in determining a TWA metric. Methods for acquiring T-wave signals for TWA assessment are generally described in U.S. Pat. Pub. No. 2006/0116592 (Zhou, et al.), incorporated herein by reference in its entirety. 
     At block  215 , T-wave measurements are performed using the acquired T-wave signals. T-wave measurements may include detecting any feature of the T-wave signal known to alternate during a TWA episode. Among the T-wave measurements that may be made are T-wave amplitude and T-wave area (integral). In one embodiment of the present invention, at least two amplitudes are determined from each T-wave signal. The amplitudes determined from each T-wave signal may be stored in a T-wave matrix in which T-waves are assigned an A or B label in an alternating manner as generally disclosed in U.S. Patent Publication No. 2006/0116596 (Zhou, et al.), hereby incorporated herein by reference in its entirety. 
     Two signal amplitudes determined for each T-wave signal may be a maximum peak and a minimum peak amplitude to enable determination of a difference in peak-to-peak amplitudes of alternating T-wave signals. Alternatively, the two or more amplitudes are determined from signal samples occurring at spaced apart intervals along the T-wave signal. In still other embodiments, amplitudes of T-wave signal sample points identified as fiducial points of the T-wave signal or at specified intervals relative to a reference time point may be determined. 
     At block  220 , differences between corresponding sample point amplitudes in a pair of consecutive T-wave signals are computed. These differences are used at block  225  for computing a TWA metric. 
     As described in the previously-incorporated &#39;592 published application, the T-wave measurements may be evaluated for possible contamination due to artifacts or signal noise. This evaluation is based on the differences between “A” and “B” T-wave measurements occurring in the T-wave signals. If TWA is present, the differences in the “A” and “B” T-wave measurements will be consistent in phase evidencing an A-B-A-B-A-B pattern. For example, if T-wave amplitudes are measured, the “A” T-wave amplitudes will be greater than the “B”T-wave amplitudes most of the time or less than the “B” T-wave amplitudes most of the time. Considerable variation in the comparative relation of the “A” and “B” T-wave measurements does not evidence an alternans pattern. As such, determination of a TWA metric may include verification that the beat-to-beat differences between “A” and “B” T-wave measurements are consistent in phase. If the differences are changing in phase, i.e., “A” measurements are sometimes greater and sometimes less than “B” measurements, the TWA measurement may not be clinically meaningful. The TWA consistency may be evaluated by determining the percentage of all beat-to-beat differences being of the same phase. The TWA assessment may further include detecting premature ventricular contractions and eliminating data corresponding to T-wave signal pairs that include a premature ventricular contraction. 
       FIG. 5A  is a diagram of superimposed consecutive T-wave signals illustrating the presence of TWA. The consecutive T-wave signals form a T-wave signal pair that can be evaluated for detecting TWA. The T-wave signals  240  and  242  have been obtained using minimal filtering, e.g., wide band filtering of 0.05 to 1000 Hz, as might be used in external ECG monitoring. The T-wave signals  240  and  242  are monophasic signals, and the T-wave signal “A”  240  has a greater maximum peak amplitude than the T-wave signal “B”  242 . Methods used in past practice, such as the Modified Moving Average method, have relied on the maximum peak amplitude difference  244  between T-wave pairs in detecting the presence of TWA. 
       FIG. 5B  is a diagram of superimposed consecutive T-wave signals acquired during TWA using narrow band filtering, e.g. 3 to 100 Hz filtering, as might be used in ICDs. The consecutive T-wave signals  245  and  246  form a T-wave signal pair that can be evaluated for detecting TWA. The T-wave signals  245  and  246  become biphasic signals after filtering by the ICD. T-wave signal “A”  245  has a greater maximum peak amplitude than the T-wave signal “B”  246 , however the difference  247  is diminished relative to the peak difference  244  of the relatively unfiltered signals  240  and  242  of  FIG. 5A . The magnitude of the TWA is somewhat reduced due to the ICD filtering methods and the total effect of the alternans on the T-wave signal is divided between the two phases of the filtered EGM signals  245  and  246 . As such, an amplitude difference  248  is also present between the minimum peak amplitudes of the signals  245  and  246 . Methods used in past practice, such as the Modified Moving Average method, which rely on the maximum peak amplitude difference  247 , and do not take into account amplitude differences of a minimum peak or other T-wave signal points, will be less sensitive to detecting TWA when applied to ICD filtered T-wave signals. 
       FIG. 6A  is a diagram of a pair of T-wave signals illustrating one method for evaluating T-wave signal pairs for detecting TWA in an implantable device. The T-wave signals shown in  FIG. 6A  include a first T-wave labeled as an “A” T-wave signal  250  followed consecutively by a second T-wave labeled as a “B” T-wave signal  252 . For each T-wave signal  250  and  252 , two amplitudes are determined. For the first T-wave signal  250 , a maximum peak amplitude  260  and a minimum peak amplitude  262  is determined. Likewise, for the next consecutive T-wave signal  252 , the maximum peak amplitude  264  and the minimum peak amplitude  266  are determined. In one embodiment of the invention, the difference between the peak maximum amplitudes  260  and  264  and the difference between the peak minimum amplitudes  262  and  266  for the T-wave signals  250  and  252  are determined for use in computing a metric of TWA. 
       FIG. 6B  is a diagram of a pair of T-wave signals  270  and  272  illustrating an alternative method for evaluating T-wave signal pairs for detecting TWA in an implantable device. TWA can manifest during the ascending or descending phases of a T-wave signal. As such, in some embodiments, measurements of signal point amplitudes along the ascending and/or descending phases of the T-wave signal, in addition to or alternatively to detecting maximum and minimum peak amplitudes, may result in greater sensitivity to detecting TWA, especially when TWA is not occurring at the peak of T wave. 
     T-wave signals  270  and  272  are sensed during consecutive T-wave sensing windows  271  and  273 , respectively. A number of signal amplitudes  290  and  292  are determined from each T-wave signal  270  and  272  along the ascending and descending phases of the T-wave signals  270  and  272 . A first signal amplitude  290   a  may be determined from the first T-wave signal  270  at a predetermined interval  276  relative to the start  274  of the T-wave sensing window  271 . A number of signal amplitudes  290  are determined thereafter at time intervals  278 , which may be equally spaced time intervals. The number of signal amplitudes determined and the time intervals  276  and  278  used may vary between embodiments. 
     Likewise a first signal amplitude  292   a  is determined at a time interval  282  following the start  280  of sensing window  273 . A number of signal amplitudes  292  are determined thereafter at time intervals  284 . The difference between the amplitudes  290  and respective amplitudes  292  occurring at corresponding time intervals during the consecutive T-wave sensing windows  270  and  272  are computed for use in determining TWA metric. 
       FIG. 7  is an EGM signal indicating sliding signal analysis windows encompassing a selected number of T-wave signal pairs. In one embodiment of the invention, computations for determining differences between the amplitudes of selected T-wave signal sample points are performed for a predetermined number of T-wave signal pairs during a sliding analysis window. For example, a first set of five consecutive T-wave signal pairs may be obtained during the first analysis window  302  which includes  10  cardiac cycles. The analysis window  304  also includes  10  cardiac cycles from which another set of five consecutive pairs of T-wave signals may be analyzed. Analysis window  304  is shifted in time relative to analysis window  302 . Analysis windows  302 ,  304  and  306  may be overlapping as shown in  FIG. 5 . Alternatively, analysis windows  302 ,  304  and  306  may be consecutive, non-overlapping analysis windows and may be spaced apart in time or having starting times coinciding with the end time of the preceding window. While five T-wave signal pairs are shown in each analysis window  302 ,  304  and  306 , it is recognized that an analysis window may be set to include any number of T-wave signal pairs, for example 1 pair, 5 pairs, 10 pairs, 20 pairs or any other selected number of signal pairs. Furthermore, analysis windows  302 ,  304  and  306  may include the same or a different number of T-wave signal pairs. 
     It is recognized that the analysis windows  302 ,  304 , and  306  may be automatically extended when premature contractions or noise is detected during T-wave signal acquisition in order to allow collection of a predetermined number of T-wave signal pairs needed for performing the TWA assessment. Alternatively, if an analysis window is thought to be corrupt due to noise or premature contractions, the analysis window may be discarded and a new analysis window may be applied to obtain new data. 
       FIG. 8  is a flow chart of a method for determining a TWA metric for detecting the presence of TWA. At block  402 , a TWA measurement is initiated. A counter Y for counting the number of analysis windows that have been applied during the assessment is initialized to zero. A counter N for counting the then number of T-wave signals to be analyzed during each analysis window is also initialized to zero. The total number of analysis windows, Z, and the total number of T-wave signals, X, to be included in each analysis window are set to desired values. For example, in one embodiment, three analysis windows each including five T-wave signal pairs (ten cardiac cycles total) may be analyzed. 
     At block  404 , the current analysis window counter value, Y, is compared to the total number of windows to be processed Z. If the window counter Y has not reached Z, the analysis window counter Y is incremented by 1. At block  408 , the T-wave signal counter N is compared to the number of T-wave signals, X, to be evaluated in each analysis window. Initially, both Y and N counters are set at zero and will be incremented at blocks  406  and  410  respectfully. 
     At block  412  the Nth EGM signal is acquired. The R-wave present in the EGM signal is detected at block  414  allowing the T-wave sensing window to be set to acquire the Nth T-wave signal at block  416 . The T-wave signal is evaluated at block  418  to determine the amplitude of at least two signal data points. As described previously, the amplitudes of selected T-wave signal data points may include a maximum and a minimum peak data points, temporally spaced data points, or other selected data points from the T-wave signal. 
     In one embodiment the peak-to-peak amplitude of the T-wave is determined at block  420  using the maximum peak amplitude and minimum peak amplitude. At block  422 , the Nth T-wave signal being evaluated is determined to be either an odd numbered T-wave of the X T-wave signals to be evaluated in the current analysis window or an even numbered T-wave. If the Nth T-wave signal being evaluated is an odd numbered T-wave, or “A” beat, the amplitudes of the selected data points are stored as the A N  beat data at block  426 . If the Nth T-wave signal being evaluated is an even numbered T-wave, or “B” beat, the amplitude data is stored as the B N  beat data at block  424 . 
     Once the amplitude data is saved for the current T-wave signal, method  400  returns to block  408  to determine if the N counter has reached the total number of T-wave signals X to be evaluated in the current analysis window. If not, blocks  410  through  426  are repeated until all beats, for example 5 to 10 beats, have been evaluated. 
     If the counter N equals the number of T-wave signals to be evaluated, X, as determined at block  408 , the TWA metric is computed at blocks  428  and  429  for the current analysis window. In order to compute the TWA metric, amplitude differences between corresponding data points for each T-wave signal pair, including an A beat and the consecutive B beat, are summed at block  428 :
 
TWA( N,N+ 1) N=1,3,5 . . . X =Σ i=1,m ( A   i   −B   i )  {1}
 
     where TWA (N, N=1) is the TWA metric computed for the consecutive pairs of T-wave signals including one odd N beat, or “A” beat, and the following even N+1 beat, or “B” beat. This TWA metric for each A−B beat pair is computed by summing the differences between the amplitudes of respective i th  data points of the A and B beats, wherein m is the total number of data point amplitudes determined for each beat. When only the maximum and minimum peak amplitudes are determined for each T-wave signal, the TWA metric for an individual beat pair is computed as the sum of the difference between the maximum peaks of the A and B beats and the difference between the minimum peaks of the A and B beats. Alternatively, the TWA metric for the individual beat pair may be computed as the difference between peak-to-peak amplitudes of the A and B beats determined at block  420 . 
     An average TWA metric is computed at block  429  for the current analysis window by determining the average of the TWA metric computed for the X/2 individual beat pairs: 
     
       
         
           
             
               
                 
                   
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     where TWA(Y j ) is the average TWA metric for the current analysis window Y j  and TWA(N, N+1) are the TWA metrics computed according to Equation {1} above for T-wave signal pairs included in the current analysis window. The individual TWA metrics computed for all of the beat pairs (1 through X/2) are summed and divided by the number of T-wave signal pairs (X/2). 
     In an alternative embodiment, the TWA metric for the current analysis window can be computed at blocks  428  and  429  as the sum of the differences between averaged amplitudes of the respective i th  data points as given by Equation {3}:
 
TWA( Y   j )=|Σ i=1,m [{(sum  A   i )/( X/ 2)}−{(sum  B   i )/( X/ 2)}]|  {3}
 
     where {sum A i (X/2)} is the average of the i th  data point amplitudes for all of the “A” beats in the analysis window Y j , and {sum B i /(X/2)} is the average of the i th  data point amplitudes for all of the “B” beats in the analysis window Y j . The difference between the averaged data point amplitudes of the A beats and the B beats is summed for all of the m data point amplitudes determined for a T-wave sensing window to compute the total TWA metric for the current analysis window Y j . For example, when a peak maximum amplitude and a peak minimum amplitude are determined for each beat, an average peak maximum amplitude for all the A beats and an average peak maximum amplitude for all the B beats are computed. Likewise, average peak minimum amplituded for all the A beats and for the all of the B beats are computed. The difference between the average maximum peak amplitude of the A beats and the average maximum peak amplitude of the B beats is summed with the difference between the average minimum peak amplitude of the A beats and the average minimum peak amplitude of the B beats to yield TWA(Y j ). 
     To reduce the effects of noise, which can introduce a large A−B difference, the TWA metric for a given analysis window can be computed at block  429  after rejecting the T-wave signal pair having the largest i th  signal point amplitude difference and the T-wave signal pair having the smallest i th  signal point amplitude difference. Alternatively, the largest and smallest TWA computed for individual beat pairs, for example according to Equation 1 above, can be rejected before computing the TWA metric for the analysis window. 
     After computing the average TWA metric, TWA(Y j ), for the T-wave signal pairs included in the current analysis window, the window counter Y is compared to the total number of windows to be processed, Z, at block  404 . If all Z analysis windows have been applied, the percentage of analysis windows resulting in an average TWA metric greater than a predetermined TWA detection threshold is determined. For example, if more than 50% of the analysis windows result in an average TWA metric greater than the TWA detection threshold, sustained TWA is detected at block  434 . If less than 50% of the analysis windows result in an averaged TWA metric greater than the threshold, non-sustained TWA, or no TWA, is detected at block  432 . 
     A response to the detection of sustained TWA may be provided at block  436 , such as generating an alert signal or triggering or adjusting a patient therapy. At block  438  a TWA trend report may be updated to report the detection of sustained or non-sustained TWA. In one embodiment, a TWA burden may be tracked as the total number of analysis windows that meet the TWA detection threshold over a monitoring period, for example over 24 hours, during one night, during one week or any other selected monitoring period. A monitoring period may include continuous or intermittent bouts of TWA assessments. For example, over a one week monitoring period, TWA assessment may include 10 analysis windows applied daily on a scheduled basis or 70 analysis windows total. Alternatively or additionally, the TWA assessment may include automatically triggered TWA assessment. 
     The TWA burden can be an indicator of the severity of cardiac electrical instability or cardiac disease progression. The TWA burden can be expressed as an average TWA metric times the total analysis window length, resulting in units of μV×minutes. Alternatively the TWA burden may be computed as the sum of the total time interval of all the analysis windows meeting the TWA threshold criteria, resulting in units of time. The TWA burden may also be compared to a severity threshold which may trigger an alarm or other response by the device. 
       FIG. 9A  is a graph  500  of simulation results of TWA measurements (y-axis  510 ) performed using FFT and MMA methods compared to using a two-point amplitude assessment as illustrated in  FIG. 5A  wherein a maximum peak and minimum peak amplitude are both compared between paired T-waves. The T-wave assessment was performed using input T-wave signals presenting TWA of magnitude 0 to 100 microvolts (x-axis  508 ). The input T-wave signals correspond to biphasic T-wave signals following ICD sensing circuitry processing. TWA measured  502  using the FFT method amplifies input TWA by a constant factor (slope=1.78). In the time domain, the MMA method attenuates the measured TWA  506  (slope=0.47) because the difference between the A and B beats is measured only at one peak of the biphasic T-wave resulting in an underestimate of the TWA magnitude. TWA measurements  504  made using a two-point analysis method as described herein, specifically one which computes a difference between the biphasic maximum and minimum peaks, is neither amplified nor attenuated (slope=1). 
       FIG. 9B  is a graph  520  of TWA measurements during simulated noise input  528  using FFT  522 , MMA  526  and the two point analysis method  524  as described herein which compares both the maximum and minimum peaks in a biphasic T-wave signal. The MMA method  526  results in TWA measurements that are overestimated in the presence of input noise. As such, the MMA method  526  is the most sensitive to noise and the most likely to overestimate low-amplitude TWA and thus over-detect the presence of TWA. 
     Thus, a system and method for detecting TWA have been presented in the foregoing description with reference to specific embodiments. It is appreciated that various modifications to the referenced embodiments may be made without departing from the scope of the invention as set forth in the following claims.