The Bin Area Method: a computationally efficient technique for analysis of ventricular and atrial intracardiac electrograms.
Recent studies have reported a significant false positive rate in delivery of therapy by implantable antitachycardia devices utilizing detection algorithms based on sustained high rate. More selective decision schemes for the recognition of life-threatening arrhythmias have been recently proposed that use analysis of the intrinsic electrogram rather than rate alone. Morphological discrimination of abnormal electrograms using correlation waveform analysis (CWA) has been proposed as an effective method of intracardiac electrogram analysis, but its computational demands limit its use in implantable devices. A new method for intracardiac electrogram analysis, the bin area method (BAM), was created to detect abnormal cardiac conduction with computational requirements of one-half to one-tenth those of CWA. Like CWA, BAM is a template matching method that is sensitive to conduction changes revealed in the electrogram morphology and is independent of amplitude and baseline fluctuations. Performance of BAM and CWA were compared using bipolar right ventricular and right atrial electrode recordings from 47 patients undergoing clinical cardiac electrophysiology studies. Nineteen patients had 31 distinct monomorphic ventricular tachycardias (VTs) induced (group I), thirteen patients had paroxysmal bundle branch block of supraventricular origin (BBB) induced (group II), and 19 patients had retrograde atrial activation during right ventricular overdrive pacing (group III). (One patient was common to all three groups, and two patients were common to groups II and III.) Using the ventricular electrogram, both BAM and CWA distinguished VT from sinus rhythm in 28/31 (90%) cases, and BBB from Normal Sinus Rhythm (NSR) in 13/13 (100%) patients. Using the atrial electrogram, both BAM and CWA distinguished anterograde from retrograde atrial activation in 19/19 (100%) patients. BAM achieves similar performance to CWA with significantly reduced computational demands, and may make real-time analysis of intracardiac electrograms feasible for implantable pacemakers and antitachycardia devices.
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor.
R D
RD
Throne
J M
JM
Jenkins
L A
LA
DiCarlo
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000517:prevention & control; D002037:Bundle-Branch Block / Q000175:diagnosis; D002304:Cardiac Pacing, Artificial; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D010138:Pacemaker, Artificial; D012815:Signal Processing, Computer-Assisted; D013610:Tachycardia / Q000175:diagnosis