Real-Time Cardiac Arrhythmia Detection Using WOLA Filterbank Analysis of EGM Signals
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Research Article Real-Time Cardiac Arrhythmia Detection Using WOLA Filterbank Analysis of EGM Signals Hamid Sheikhzadeh, Robert L. Brennan, and Simon So AMI Semiconductor Canada Company, 611 Kumpf Drive, Unit 200, Waterloo, Ontario, Canada N2V 1K8 Received 27 April 2006; Revised 13 October 2006; Accepted 13 October 2006 Recommended by William Allan Sandham Novel methods of cardiac rhythm detection are proposed that are based on time-frequency analysis by a weighted overlap-add (WOLA) oversampled filterbank. Cardiac signals are obtained from intracardiac electrograms and decomposed into the timefrequency domain and analyzed by parallel peak detectors in selected frequency subbands. The coherence (synchrony) of the subband peaks is analyzed and employed to detect an optimal peak sequence representing the beat locations. By further analysis of the synchrony of the subband beats and the periodicity and regularity of the optimal beat, various possible cardiac events (including fibrillation, flutter, and tachycardia) are detected. The Ann Arbor Electrogram Library is used to evaluate the proposed detection method in clean and in additive noise. The evaluation results show that the method never misses any episode of fibrillation or flutter in clean or in noise and is robust to far-field R-wave interference. Furthermore, all other misclassification errors were within the acceptable limits. Copyright © 2007 Hamid Sheikhzadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
The objective of this research is rhythm classification and event detection based on the intracardiac electrogram (EGM) signals. The proposed methods are designed for implantable devices that should operate on extremely lowpower budgets. In the meantime, these methods should operate in real time and the processing delay should be in the minimal range acceptable for such applications. The detection methods should be very reliable and robust to interference, noise, and morphology variations. Current practical methods of cardiac rhythm detection employed in implantable cardioverter defibrillators (ICDs) are generally based on beat-by-beat time-domain analysis. Although research has been conducted to exploit more sophisticated signal processing such as wavelet transform and template matching for event detection [1–3], the new methods have rarely been employed in practical systems due to their computational and power demands and issues related to the reliability of their detection. Current challenges in reliable rhythm detection for implantable cardiac rhythm management (CRM) systems such as ICDs are the following.
(1) Inappropriate device therapy (IDT) amount to a considerable rate (between 10 to 30%) in various devices [4]. IDTs occur due to low EGM signal quality, sinus tachycardia, supraventricular tachycardia (SVT), myopotential interference, external interference,
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