Electrocardiogram classification method based on SVM

被引：1

作者：

Tang, Xiao ^{[1
]}

Mo, Zhiwen ^{[1
]}

机构：

[1] Sichuan Normal Univ, Coll Math & Software Sci, Chengdu 610066, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年

关键词：

support vector machine; feature extraction; electrocardiogram classification; CAD;

D O I：

10.2991/iske.2007.282

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Heart disease is one of the main diseases threatening human beings health, and electrocardiogram is the important basis of diagnosing cardiovascular disease. Therefore, the computer auto analysis of ECG remains the research hotspot in medical Engineering. Since the distinctiveness and variability of QRS wave, many present ECG classification techniques exist difficulties to realize. Although many methods could work successfully in recognizing certain types of ECG signals, the recognition rate usually can not be substantially promoted throughout all kinds of ECG signals. In this paper, 1-vs-rest algorithm of SVM is used for ECG classification. The algorithm for ECG classification is tested with the data of MIT-BIH. Finally a high recognize rate is obtained. It is better than normal way in constructing algorithm model and classification speed.

引用

页数：1

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