Real-time cardiac arrhythmia detection using WOLA filterbank analysis of EGM signals

Abstract

Novel methods of cardiac rhythm detection are proposed that are based on time-frequency analysis by aweighted overlap-add (WOLA) oversampled filterbank. Cardiac signals are obtained from intracardiac electrogramsand decomposed into the time-frequency domain and analyzed by parallel peak detectors in selected frequency subbands. Thecoherence (synchrony) of the subband peaks is analyzed and employed to detect an optimal peak sequence representing thebeat locations. By further analysis of the synchrony of the subband beats and the periodicity and regularity of the optimalbeat, various possible cardiac events (including fibrillation, flutter, and tachycardia) are detected. The Ann ArborElectrogram Library is used to evaluate the proposed detection method in clean and in additive noise. The evaluation resultsshow that the method never misses any episode of fibrillation or flutter in clean or in noise and is robust to far-field R-waveinterference. Furthermore, all other misclassification errors were within the acceptable limits.