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30 October 1997 Electrogram analysis during atrial fibrillation using wavelet and neural network techniques
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Abstract
Atrial fibrillation (AF) is a common arrhythmia associated with many heart diseases and has a high rate of incidence in the older population. Many of the symptoms of AF are poorly tolerated by patients and if not properly managed, may lead to fatal conditions like embolic stroke. The atrial electrograms during AF show a high degree of non- stationarity. AF being progressive in nature, we aim to link the degree of non-stationarity of the atrial electrogram to the stage of advancement of the disease, the duration of episodes of AF, possibility of spontaneous reversion to sinus rhythm and the defibrillation energy requirement. In this paper we describe a novel algorithm for classifying bipolar electrograms from the right atrium of sheep into four groups--normal sinus rhythm, atrial flutter, paroxysmal AF, chronic AF. This algorithm uses features derived from a wavelet transform representation of the signal to train an artificial neural network which is then used to classify the different arrhythmia. The success rates achieved for each subclass indicates that this approach is well suited for the study of the atrial arrhythmia.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anupama Govindan, Guang Deng, and John Power "Electrogram analysis during atrial fibrillation using wavelet and neural network techniques", Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); https://doi.org/10.1117/12.279706
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