Multiscale Hidden Markov Model applied to ECG segmentation

被引：0
|
作者：
Graja, S [1 ]
Boucher, JM [1 ]
机构：
[1] ENST Bretagne, INSERM ERT 02, F-29285 Brest, France
来源：
2003 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS: FROM CLASSICAL MEASUREMENT TO COMPUTING WITH PERCEPTIONS | 2003年
关键词：
ECG; Hidden Markov Model; Wavelet Tree; segmentation;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new electrocardiogram (ECG) segmentation method is proposed, which uses a Wavelet Tree Hidden Markov Model. The principle of this approach is, on one hand, to use wavelet coefficients to characterize the different ECG waves, and, on the other hand. to link these coefficients by a tree structure permitting to detect wave changes. By associating this method to a fusion method between scales based on the context concept, good results are obtained on a special database created for risk analysis of atrial fibrillation, particularly in P wave segmentation.
引用
收藏
页码：105 / 109
页数：5
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