Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis

作     者：GE Ding-Fei HOU Bei-Ping XIANG Xin-Jian GE Ding-Fei;HOU Bei-Ping;XIANG Xin-Jian

作者机构：School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou 310012 P. R. China 

出 版 物：《自动化学报》 (Acta Automatica Sinica)

年 卷 期：2007年第33卷第5期

页      面：462-466页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 10[医学] 

基　　金：Supported by Natural Science Foundation of Zhejiang Province of P.R.China(Y104284) 

主　　题：自动诊断 多元自回归模型 特征提取 心电图 

摘      要：This article explores the ability of multivariate autoregressive model(MAR)and scalar AR model to extract the features from two-lead electrocardiogram signals in order to classify certain cardiac *** classification performance of four different ECG feature sets based on the model coefficients are *** data in the analysis including normal sinus rhythm, atria premature contraction,premature ventricular contraction,ventricular tachycardia,ventricular fibrillation and superventricular tachyeardia is obtained from the MIT-BIH *** classification is performed using a quadratic diacriminant *** results show the MAR coefficients produce the best results among the four ECG representations and the MAR modeling is a useful classification and diagnosis tool.