Electroencephalogram pattern recognition using fuzzy logic

Author

Hu, Jung ; Knapp, Benjamin

Author_Institution

Dept. of Electr. Eng., San Jose State Univ., CA, USA

fYear

1991

fDate

4-6 Nov 1991

Firstpage

805

Abstract

A fuzzy logic approach to the classification of human sleep using electroencephalogram (EEG) data is presented. In this approach, frequency and amplitude information from an epoch of the EEG signal are extracted into a vector that is then compared to previously taught vectors representing the canonical features of six stages: wakefulness, rapid eye movement (REM) sleep, and four nonREM sleep stages. For each stage, membership functions are calculated in each epoch. The stage with the maximum degree of membership is scored and classified. The system is implemented in software using the C programming language. Analysis of about 1101 epochs of the EEG data yielded an overall agreement of 77% between the program and a human scorer

Keywords

computerised pattern recognition; electroencephalography; fuzzy logic; C programming language; EEG data; EEG signal; REM sleep; amplitude information; electroencephalogram; frequency information; fuzzy logic; human sleep classification; membership functions; nonREM sleep; pattern recognition; rapid eye movement; software; vector; wakefulness; Data mining; Electroencephalography; Frequency; Fuzzy logic; Fuzzy sets; Humans; Learning systems; Pattern classification; Pattern recognition; Sleep;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-2470-1

Type

conf

DOI

10.1109/ACSSC.1991.186558

Filename

186558