Detection and identification of cardiac arrhythmias using an adaptive, linear-predictive filter

In order to detect an abrupt change in morphology of intracardiac electrograms, the authors have developed a supplemental algorithm to rate criteria which utilizes the mean-squared error of an adaptive, linear-predictive filter (ALPF). Further analysis of the filter error is invoked when such a change is detected to identify the rhythm. ALPF successfully identified 10/11 (91%) cases of ventricular tachycardia (VT), 6/8 (75%) cases of ventricular fibrillation (VF), and 4/4 (100%) cases of sinus tachycardia (ST). As a basis of comparative analysis, correlation waveform analysis (CWA) was used to identify the same test set of arrhythmias. CWA correctly identified 11/11 (100%) cases of VT, 4/8 (50%) cases of VF, and 4/4 (100%) cases of ST. When compared to CWA, ALPF has similar accuracy in differentiating VT and ST. ALPF appears to be superior to CWA in identifying VF