Classification of Arrhythmic Events in Ambulatory Electrocardiogram, Using Artificial Neural Networks

✍ Scribed by R. Silipo; M. Gori; A. Taddei; M. Varanini; C. Marchesi

• Publisher: Elsevier Science
• Year: 1995
• Tongue: English
• Weight: 419 KB
• Volume: 28
• Category: Article
• ISSN: 0010-4809
• DOI: 10.1006/cbmr.1995.1021

✦ Synopsis

We propose artificial neural networks (ANN) for ambulatory ECG arrhythmic event classification, and we compare them with some traditional classifiers (TC). Among them, the one based on the median method (heuristic algorithm) was chosen and taken as a quality reference in this study, while a back propagation based classifier, designed as an autoassociator for its peculiar capability of rejecting unknown patterns, was examined. Two tests were performed: the first to discriminate normal vs ventricular beats and the second to distinguish among three classes of arrhythmic events. The results show that the ANN approach is more reliable than the traditional classifiers in discriminating among many classes of arrhythmic events: (98 %) by ANN vs (99 %) by a TC for correctly classified normal beats, (98 %) by ANN vs (96 %) by TC for correctly classified ventricular ectopic beats, (96 %) by ANN vs (59 %) by TC for correctly classified supraventricular ectopic beats, and (83 %) by ANN vs (86 %) by median method for correctly classified aberrated atrial premature beats. This paper also tackles the problem of the management of classification uncertainty. Two concurrent uncertainty criteria have been introduced, to reduce the classification error of the unknown ventricular and supraventricular arrhythmic beats respectively. The error in ventricular beats case was kept close to (0 %) in average and for supraventricular beats was kept at (35 %) in average. So we can state that the ANN approach is powerful in classifying beats represented in the training set and that it manages the uncertainty in such a way as to reduce, in any case, the global error percentage. (\odot 1995) Academic Press, Inc.