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Báo cáo hóa học: " Research Article Real-Time Cardiac Arrhythmia Detection Using WOLA Filterbank Analysis of EGM Signals"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Real-Time Cardiac Arrhythmia Detection Using WOLA Filterbank Analysis of EGM Signals | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 76256 8 pages doi 10.1155 2007 76256 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 N2V1K8 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. 1. 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