Silipo R, Gori M, Taddei A, Varanini M, Marchesi C
Dipartimento di Sistemi e Informatica, Universitá di Firenze, Italy.
Comput Biomed Res. 1995 Aug;28(4):305-18. doi: 10.1006/cbmr.1995.1021.
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.
我们提出使用人工神经网络（ANN）对动态心电图心律失常事件进行分类，并将其与一些传统分类器（TC）进行比较。其中，选择了基于中位数方法（启发式算法）的分类器，并将其作为本研究中的质量参考，同时研究了一种基于反向传播的分类器，该分类器因其具有拒绝未知模式的特殊能力而被设计为自联想器。进行了两项测试：第一项测试用于区分正常心跳与室性早搏，第二项测试用于区分三类心律失常事件。结果表明，在区分多类心律失常事件方面，ANN方法比传统分类器更可靠：对于正确分类的正常心跳，ANN的准确率为98%，而TC为99%；对于正确分类的室性早搏，ANN为98%，TC为96%；对于正确分类的室上性早搏，ANN为96%，TC为59%；对于正确分类的房性早搏伴室内差异性传导，ANN为83%，中位数方法为86%。本文还解决了分类不确定性的管理问题。引入了两个并行的不确定性标准，分别用于减少未知室性和室上性心律失常心跳的分类误差。室性心跳的误差平均保持在接近0%，室上性心跳的误差平均保持在35%。因此，我们可以说，ANN方法在对训练集中表示的心跳进行分类方面很强大，并且它以一种在任何情况下都能降低全局误差百分比的方式来管理不确定性。
