ECG beat classification using Mirrored Gauss Model

被引：0

作者：

Zhou, Qunyi ^{[1
]}

Liu, Xing ^{[1
]}

Duan, Huilong ^{[1
]}

机构：

[1] Zhejiang Univ Sci & Technol, Dept Informat Technol & Elect Engn, Hangzhou 310012, Zhejiang, Peoples R China

来源：

2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 | 2005年

关键词：

arrithmias beat classification; Gauss function; curving fitting;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A novel classification algorithm of the ECG beats, applying Mirrored Gauss Model (MGM) had been proposed in this paper. The MGM has strong morphological representation ability for QRS complex waves using curve fitting. With the MGM, the width of QRS complex wave could be extracted and applied to ECG beat classification easily, effectively and automatically. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that the MGM is a promising algorithm for ECG beat classification. The whole classification accuracy is 93.93% for normal beats and 93.94% for premature ventricular contraction (PVC) beats.

引用

页码：5587 / 5590

页数：4

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