Hidden in Markov models in biomedical signal processing

被引：0
作者：
Cohen, A [1 ]
机构：
[1] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-84105 Beer Sheva, Israel
来源：
PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 20, PTS 1-6: BIOMEDICAL ENGINEERING TOWARDS THE YEAR 2000 AND BEYOND | 1998年 / 20卷
关键词：
Hidden Markov Models; Continuous Density; Discrete Density; Markov chain; pathological speech; EEG; ECG;
D O I：
暂无
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
Hidden Markov Models (HMM) are statistical models used very successfully and effectively in speech processing. The model is however a general model for stochastic processes and may thus be applied to a large variety of biomedical signals. The paper provides an in depth tutorial on HMM and its applications to biomedical signal processing. Discrete Density (DD-HMM) and Continuous Density HMM (CD-HMM) are presented, the various algorithms required for training the model, for estimating the optimal state sequence and the observation probabilities are discussed. The HMM have not been widely applied to biomedical signal processing. The paper reviews some of the applications, and discusses potential applications.
引用
收藏
页码：1145 / 1150
页数：6
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