ECG beat classification based on a cross-distance analysis

被引:0
|
作者
Shahram, M [1 ]
Nayebi, K [1 ]
机构
[1] Sharif Univ Technol, Dept Elect Engn, Tehran 11365, Iran
来源
ISSPA 2001: SIXTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1 AND 2, PROCEEDINGS | 2001年
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper presents a multi-stage algorithm for QRS Complex classification into normal and abnormal categories using an unsupervised sequential beat clustering and a cross-distance analysis algorithm. After the sequential beat clustering, a search algorithm based on relative similarity of created classes is used to detect the main normal class. Then other classes are labeled based on a distance measurement from the main normal class. Evaluated results on MIT-BIH ECG database exhibits an error rate less than 1% for normal and abnormal discrimination and 0.2% for clustering of 15 types of arrhythmia existed in MIT-BIH database.
引用
收藏
页码:234 / 237
页数:4
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