Hidden Markov models in biomedical signal processing

Author

Cohen, A.

Author_Institution

Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Volume

3

fYear

1998

fDate

29 Oct-1 Nov 1998

Firstpage

1145

Abstract

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 HMMs have not been widely applied to biomedical signal processing. The paper reviews some of the applications, and discusses potential applications

Keywords

electrocardiography; electroencephalography; finite state machines; hidden Markov models; medical signal processing; probability; reviews; speech processing; ECG; EEG; HMM based recognition systems; biomedical signal processing; continuous density HMM; discrete density HMM; hidden Markov models; laryngeal pathologies; model training algorithms; observation probabilities; optimal state sequence; pathological speech; protein sequence analysis; sleep staging; speech disorders; statistical models; three states Markov chain model; tutorial; Biomedical computing; Biomedical signal processing; Brain modeling; Electrocardiography; Electroencephalography; Hidden Markov models; Signal processing; Signal processing algorithms; Stochastic processes; Switches;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Conference_Location

Hong Kong

ISSN

1094-687X

Print_ISBN

0-7803-5164-9

Type

conf

DOI

10.1109/IEMBS.1998.747073

Filename

747073