Hidden process modeling

Author

Kosanovic, Bogdan R. ; Chaparro, Luis E. ; Sclabassi, Robert J.

Author_Institution

Lab. for Comput. Neurosci., Pittsburgh Univ., PA, USA

Volume

5

fYear

1995

fDate

9-12 May 1995

Firstpage

2935

Abstract

Presents a method that is a generalization of hidden Markov modeling for the situations where elementary events cannot be clearly defined. A family of fuzzy sets, induced on a temporal universe, is used to model the dynamic trajectory of a physical system as a collection of hidden processes that coexist at the same time, but to different degrees. An algorithm based on unsupervised pattern recognition that estimates the prototypes and activities of the hidden processes is presented. The performance of the method is illustrated using experimental data obtained from electroencephalographic (EEG) signals recorded during sleep

Keywords

electroencephalography; estimation theory; fuzzy set theory; hidden Markov models; medical signal processing; pattern recognition; dynamic trajectory; electroencephalographic signals; fuzzy sets; hidden Markov modeling; hidden process modeling; sleep; temporal universe; unsupervised pattern recognition; Electroencephalography; Fuzzy sets; Hidden Markov models; Mathematical model; Prototypes; Signal analysis; Signal processing; Space exploration; Stochastic processes; Surgery;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479460

Filename

479460