An Approach Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

被引：0
|
作者：
Krimi, Samar [1 ]
Ouni, Kais [1 ]
Ellouze, Noureddine [1 ]
机构：
[1] Natl Engn Sch Tunis ENIT, Lab Syst & Signal Proc LSTS, Tunis 1002, Tunisia
来源：
2008 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES: FROM THEORY TO APPLICATIONS, VOLS 1-5 | 2008年
关键词：
ECG segmentation; wavelet transform; hidden Markov models;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper highlights a new method for ECG segmentation based on the combination of two mathematical techniques namely the Wavelet Transform (WT) and Hidden Markov Models (HMM). In this method, we first localize edges in the ECG by wavelet coefficients, then, features extracted from the edges serve as input for the HMM This new approach was tested and evaluated on the manually annotated database QT database (QTDB), which is regarded as a very important benchmark for ECG analysis. We obtained a sensitivity Se=99,40% for QRS detection and a sensitivity Se=94,65% for T wave detection.
引用
收藏
页码：821 / 826
页数：6
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