Automatic segmentation of heart sound signals using Hidden Markov Models

被引：0
|
作者：
Ricke, AD [1 ]
Povinelli, RJ [1 ]
Johnson, MT [1 ]
机构：
[1] GE Healthcare, Milwaukee, WI 53223 USA
来源：
Computers in Cardiology 2005, Vol 32 | 2005年 / 32卷
关键词：
D O I：
暂无
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
The monitoring of respiration rates using impedance plethysmography is often confused by cardiac activity. This paper proposes using the phonocardiogram as an alternative, since the process of respiration affects heart sounds. As part of this research, a technique is developed to segment heart sounds into its component segments, using Hidden Markov Models. The heart sounds data is preprocessed into feature vectors, where the feature vectors are comprised of the average Shannon energy of the heart sound signal, the delta Shannon energy, and the delta-delta Shannon energy. The performance of the segmentation system is validated using eight-fold cross-validation.
引用
收藏
页码：953 / 956
页数：4
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