Neural network assisted cardiac auscultation.
Traditional cardiac auscultation involves a great deal of interpretive skill. Neural networks were trained as phonocardiographic classifiers to determine their viability in this rôle. All networks had three layers and were trained by backpropagation using only the heart sound amplitude envelope as input. The main aspect of the study was to determine what topologies, gain and momentum factors lead to efficient training for this application. Neural networks which are trained with heart sound classes of greater similarity were found to be less likely to converge to a solution. A prototype normal/abnormal classifier was also developed which provided excellent classification accuracy despite the sparse nature of the training data. Future directions for the development of a full-scale computer-assisted phonocardiographic classifier are also considered.
Cathers
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I
Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia.
D002309:Cardiology / Q000193:education; D003936:Diagnosis, Computer-Assisted; D004526:Efficiency; D006326:Heart Auscultation; D006331:Heart Diseases / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D008838:Microcomputers; D016571:Neural Networks, Computer; D012016:Reference Values; D015203:Reproducibility of Results; D012984:Software