Hu Y H, Tompkins W J, Urrusti J L, Afonso V X
Department of Electrical and Computer Engineering, University of Wisconsin-Madison 53706-1691.
J Electrocardiol. 1993;26 Suppl:66-73.
The authors have investigated potential applications of artificial neural networks for electrocardiographic QRS detection and beat classification. For the task of QRS detection, the authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex. This provided more reliable detection of QRS complexes even in a noisy environment. For electrocardiographic QRS complex pattern classification, an artificial neural network adaptive multilayer perceptron was used as a pattern classifier to distinguish between normal and abnormal beat patterns, as well as to classify 12 different abnormal beat morphologies. Preliminary results using the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital, Cambridge, MA) arrhythmia database are encouraging.
作者们研究了人工神经网络在心电图QRS波检测和心搏分类中的潜在应用。对于QRS波检测任务，作者使用自适应多层感知器结构对非线性背景噪声进行建模，以增强QRS复合波。这使得即使在有噪声的环境中也能更可靠地检测QRS复合波。对于心电图QRS复合波模式分类，使用人工神经网络自适应多层感知器作为模式分类器，以区分正常和异常的心搏模式，以及对12种不同的异常心搏形态进行分类。使用麻省理工学院/贝斯以色列医院（位于马萨诸塞州剑桥市）心律失常数据库得到的初步结果令人鼓舞。
