Optimized standby rate reduces the ventricular rate variability in pacemaker patients with atrial fibrillation

Patients with chronic atrial fibrillation (AF) and symptomatic bradycardia often receive ventricular-based pacemakers. However, many of these patients continue to have symptoms of palpitations, which may be due to ventricular rate variability. It has previously been shown that continuous ventricular pacing during AF has a stabilizing effect on the ventricular rate. Hence, a study was initiated to determine whether a patient-specific optimal ventricular standby rate that reduces the ventricular rate variability, without over-pacing, could be predicted. A ventricular rate stabilization (VRS) pacing algorithm that increases the pacing rate until instability is reduced below a threshold was developed. The VRS algorithm was utilized to determine a patient-specific standby rate in 15 patients with chronic AF, intact AV nodal conduction, and implanted pacemakers. The computer algorithm controlled a pacemaker programmer to automatically change the pacemaker's ventricular pacing rate via telemetry. Patients were studied for 15 minutes with VRS and for 15 minutes with 50 ppm fixed rate pacing (control). The results were as follows: (1) VRS versus control = P < 0.05; (2) mean ventricular pacing rate (ppm): 77 +/- 13 versus 50 +/- 0; (3) mean ventricular rate (beats/min): 82 +/- 13 versus 79 +/- 12; (4) ventricular rate coefficient of variation (%): 11 +/- 1 versus 22 +/- 5; (5) percent pacing: 75 +/- 8 versus 6 +/- 8; (6) percent of RR intervals less than minimum pacing interval eliminated: 58 +/- 12; (8) regression analysis: mean VRS pacing rate (beats/min) = 0.96 x mean control ventricular rate + 2.3, r2 = 0.85. We concluded that: (1) a moderate increase in the ventricular pacing rate was required to substantially stabilize the ventricular rate; (2) the resulting mean ventricular rate increased marginally; (3) a majority of RR cycles less than each patient's minimum pacing interval were eliminated; and (4) there was a linear relationship between the mean ventricular rate during control and the optimal ventricular pacing rate. Thus, a ventricular pacing rate close to the mean ventricular rate during control consistently reduced the ventricular variability. Although pacing at an increased ventricular standby rate reduces variability at rest, the optimal solution would likely be an adaptive rate algorithm that changes the ventricular standby rate as the mean intrinsic rate varies.

A comparative analysis of signal processing methods for motion-based rate responsive pacing

Pacemakers that augment heart rate (HR) by sensing body motion have been the most frequently prescribed rate responsive pacemakers. Many comparisons between motion-based rate responsive pacemaker models have been published. However, conclusions regarding specific signal processing methods used for rate response (e.g., filters and algorithms) can be affected by device-specific features. To objectively compare commonly used motion sensing filters and algorithms, acceleration and ECG signals were recorded from 16 normal subjects performing exercise and daily living activities. Acceleration signals were filtered (1-4 or 15-Hz band-pass), then processed using threshold crossing (TC) or integration (IN) algorithms creating four filter/algorithm combinations. Data were converted to an acceleration indicated rate and compared to intrinsic HR using root mean square difference (RMSd) and signed RMSd. Overall, the filters and algorithms performed similarly for most activities. The only differences between filters were for walking at an increasing grade (1-4 Hz superior to 15-Hz) and for rocking in a chair (15-Hz superior to 1-4 Hz). The only differences between algorithms were for bicycling (TC superior to IN), walking at an increasing grade (IN superior to TC), and holding a drill (IN superior to TC). Performance of the four filter/algorithm combinations was also similar over most activities. The 1-4/IN (filter [Hz]/algorithm) combination performed best for walking at a grade, while the 15/TC combination was best for bicycling. However, the 15/TC combination tended to be most sensitive to higher frequency artifact, such as automobile driving, downstairs walking, and hand drilling. Chair rocking artifact was highest for 1-4/IN. The RMSd for bicycling and upstairs walking were large for all combinations, reflecting the nonphysiological nature of the sensor. The 1-4/TC combination demonstrated the least intersubject variability, was the only filter/algorithm combination insensitive to changes in footwear, and gave similar RMSd over a large range of amplitude thresholds for most activities. In conclusion, based on overall error performance, the preferred filter/algorithm combination depended upon the type of activity.

A stochastic network model of the interaction between cardiac rhythm and artificial pacemaker

The electrical interaction between the heart and an artificial pacemaker is often complex. Because of the sophistication and diversity of dual-chamber device algorithms, even experienced cardiologists can have difficulty interpreting paced electrocardiograms (ECG's). In order to study heart-pacemaker interaction (HPI), a computer model of the cardiac conduction system has been developed which includes the effects of artificial pacemaker function and failure. The stochastic network model of cardiac conduction consists of five vertices, each representing a functional electrophysiologic element. Electrophysiologic multidimensional conditional probability functions determine the depolarization status of each vertex. The atrioventricular (AV) node is emulated using a mathematical model which includes the influence of past cycle lengths on AV nodal conduction time. Twenty-three classes of arrhythmias may be simulated and, for pacing simulation, one of 12 antibradycardia pacing modes may be chosen. Random effects of pacemaker malfunction including oversensing, undersensing, or failure-to-capture may be simulated through the use of probability distribution functions. This model should prove useful in the development of pacemaker algorithms, determining patient-specific pacemaker therapy, and predicting causes for apparent pacemaker malfunction. The model has been used in the development of an expert system to analyze paced ECG's for pacemaker function and malfunction.

Detection of atrial activation by intraventricular electrogram morphology analysis: a study to determine the feasibility of P wave synchronous pacing from a standard ventricular lead

The detection of atrial activation from a standard ventricular pacing lead with standard ventricular electrodes would provide patients with VVI and VVIR pacing systems atrial rate response and atrial synchrony. In addition to potentially increasing cardiac output appropriately in these patients at rest and during moderate exercise, P wave sensing with such a device could help reduce pacemaker syndrome. In this study, unipolar signals from distal and proximal intraventricular electrodes were recorded from the right ventricular apex in 20 patients. Unipolar electrograms from 16 patients were recorded using temporary electrophysiology catheters and in four patients using permanent pacemaker leads. Approximately 3 minutes of data per patient were acquired and analyzed. After selection of a P wave template, the difference in baseline normalized area between the template and signal was calculated on a point-by-point basis. The percent of atrial depolarizations correctly detected was determined for each patient and lead configuration at the optimal threshold. Far-field P wave accuracy was better at the proximal electrode (74 +/- 25%) than at the distal electrode (57 +/- 34%) (P < 0.025). At the proximal electrode, 15/20 (75%) patients had > 70% accuracy and 11/20 (55%) patients had > 80% accuracy. At the distal electrode, 10/21 (48%) patients had > 70% accuracy and 7/21 (33%) patients had > 80% accuracy. In conclusion, far-field detection of atrial activation at the ventricular proximal electrode appears possible with sufficient accuracy to provide periods of atrial rate response and synchrony in patients with a single standard lead.

Separation of ventricular tachycardia from sinus rhythm using a practical, real-time template matching computer system

Template matching morphology analysis of the intraventricular electrogram (IVEG) has been proposed for inclusion in implantable cardioverter defibrillators (ICDs) to reduce the number of false ventricular tachyarrhythmia detections caused by rate overlap between ventricular tachycardia (VT) and sinus tachycardia and/or supraventricular tachycardia. Template matching techniques have been developed that reduce the computational complexity while preserving the perceived important aspects of electrogram amplitude and baseline independence found in such computationally unsolved methods as correlation waveform analysis (CWA). These methods have been shown to work as well as CWA for separation of VT, however, they have not been proven in real-time on a system that incorporates many of the constraints of present day ICDs. The present study was undertaken with two purposes: (1) to determine if real-time IVEG template matching analysis on an ICD sensing emulator was accurate in separating VT from sinus rhythm (SR) electrograms; and (2) to compare amplitude normalized area of difference (NAD) with signature analysis (SIG), a new, computationally less expensive technique that normalizes for amplitude variation within the expected physiological level of variability. In this study, IVEGs, obtained from 16 patients who underwent electrophysiological study (EPS) for evaluation of sustained ventricular arrhythmia, were digitized to 250 Hz with 6-bit quantization after filtering (16-44 Hz) and differentiation. After an SR template was selected and periodically updated, it was compared to subsequent IVEGs using NAD and SIG. In general, SIG calculates the fraction of samples occurring outside template window boundaries. Eleven-beat running medians from beat-by-beat NAD and SIG results were determined.(ABSTRACT TRUNCATED AT 250 WORDS)

Identification of ventricular tachycardia using intracardiac electrograms: a comparison of unipolar versus bipolar waveform analysis

Implantable antitachycardia devices suffer a high false-positive rate of delivery of therapy because current detection schemes based upon ventricular rate and rate variations are excessively sensitive at the cost of specificity. Several methods have been proposed for providing complementary information derived from morphologic analysis of intraventricular electrograms in order to increase specificity. The majority of these techniques have utilized bipolar electrogram analysis to detect changes in ventricular activation indicative of ventricular tachycardia. Whether bipolar or unipolar intracardiac electrogram analysis might be preferred for discriminating ventricular tachycardia from sinus rhythm has not been determined. In this study, a previously demonstrated method for identification of ventricular tachycardia using intracardiac electrograms, correlation waveform analysis, was used to analyze both unipolar and bipolar signals during sinus rhythm and ventricular tachycardia recorded during electrophysiology studies of 15 patients with inducible sustained monomorphic ventricular tachycardia. Correlation waveform analysis consistently discriminated between all depolarizations during ventricular tachycardia in 14/15 patients (93%) using either electrogram configuration; 13 of the 14 patients were common to both groups. Of these patients, 8/15 (53%) had greater separation between sinus rhythm and ventricular waveforms with bipolar electrogram analysis while 7/15 (47%) had greater separation with unipolar electrogram analysis. We conclude that morphologic analysis of unipolar and bipolar electrograms may be equally effective in distinguishing ventricular tachycardia from sinus rhythm. For individual patients, either a unipolar or bipolar ventricular configuration may be preferable, and should be chosen on a patient-specific basis during electrophysiology study prior to antitachycardia device implantation.

Computer analysis of physiologic signals in a cardiovascular research laboratory

A comprehensive computer program which provides immediate computation and feedback has been developed for data acquisition and analysis of signals in a cardiovascular animal laboratory. The system is based on a microcomputer equipped with analog-to-digital converter and supports function modules which digitize, filter, and differentiate up to 8 simultaneously sampled cardiovascular signals. The program detects, analyses, and plots incoming and averaged beats. Beat-by-beat signal averaging for each channel is performed and cardiac cycles are partitioned automatically. For each cardiac and average cycle the amplitude at 6 physiologic fiducial markers are measured and derived calculations are made. Channel vs channel plots and loop area measurements are also computed and displayed. The computer algorithms have been shown to give accurate, precise, and reproducible results when tested on canine cardiovascular data. Also, it has been demonstrated that signal averaging is an appropriate analysis technique for cardiovascular signals.

Computerized interpretation of the paced ECG

Analysis of the paced electrocardiogram (ECG) is important to the follow-up evaluation of patients with implanted pacemakers. Because of the complexity and variability of pacemaker algorithms, diagnosis of paced ECGs is often considerably more difficult than the interpretation of usual ECGs. Automated interpretation of the paced ECG can provide great clinical benefit because few clinicians are adequately trained in the diagnosis of such ECGs for the interpretation of pacemaker functionality. However, comparatively little work has been done in this area, mainly because the diversity and complexity of pacemaker logic makes interpretation, automated or manual, a difficult task. The following paper reviews research in computer interpretation of the pacemaker ECG and presents a new automated method which yields more detailed and accurate results than any previous technique.

Detection of ventricular tachycardia using scanning correlation analysis

Cross correlation is an accurate method for distinguishing normal sinus rhythm (NSR) from ventricular arrhythmias. The computational demands of the method, however, have prohibited development of an implantable device using correlation. In this study, temporal data compression prior to correlation analysis was used to reduce the total number of computations. Unipolar and bipolar intracardiac electrograms of NSR and 23 episodes of ventricular tachycardia (VT) from 23 patients were obtained from a right ventricular apex electrode catheter during routine electrophysiology studies. The data were filtered (1-11 Hz), digitized (250 samples/sec) and temporally compressed to 50 samples/sec. Data compression removed four out of every five samples by only saving the sample with the maximum excursion from the last saved sample. The average squared correlation coefficient (r2) was computed for the NSR and VT episodes using each patient's NSR waveform as a template. In all 23 patients, the r2 values showed large separation between NSR versus VT in both unipolar (0.93 +/- 0.05 vs 0.20 +/- 0.16, P less than 0.005) and bipolar (0.91 +/- 0.07 vs 0.17 +/- 0.11, P less than 0.005) electrode configurations using template lengths of 80% the intrinsic interval (avg +/- SD). Narrow templates (40% intrinsic interval or less) often resulted in multiple r2 peaks during each heart cycle and degraded the r2 separation (n = 10, P less than 0.005). High pass filtering at 3 Hz also degraded the r2 separation (n = 10, P less than 0.05). Standard noncompressed correlations indicated that data compression had negligible effects on the results. Thus, a computationally efficient cross correlation method was found to be a reliable detector of VT.(ABSTRACT TRUNCATED AT 250 WORDS)

An algorithm for the quantification of ST-T segment variability

A template boundary algorithm which quantitatively determines repolarization (ST-T segment) variability in a normal population has been developed. The algorithm defines an initial ST-T template for comparison with successive beats. Variability is quantified using boundary limits around the template which are widened, when necessary, to included incoming ST-T segments. The boundaries at the end of each hour are stored and the collection of boundaries over a set of normal subjects quantifies the normal variation over the entire ST-T segment. The algorithm can be used to determine prospectively normal ST-T variability based on a regression analysis of R-wave or T-wave amplitude, and QT interval. Application of these boundary predictions should be useful in distinguishing repolarization changes secondary to ischemia from normal variability.

Development of a template boundary algorithm to determine the ST-T threshold for silent ischemia

A template boundary algorithm which quantitatively determines ST-T variability has been implemented and tested using seven normal patients. Relatively uniform variability was demonstrated throughout the ST-T segment in these patients during single lead continuous electrocardiographic monitoring at bedrest. The range of variability appears to be a function of both R wave and T wave amplitude. This algorithm appears to have potential utility in defining the ischemic criteria for silent ischemia.