A neural network system for automatic classification of sleep stages

Author

Sun, Mingui ; Ryan, Neal D. ; Dahl, Ronald E. ; Hsin, Hsi-Chin ; Iyengar, Satish ; Sclabassi, Robert J.

Author_Institution

Pittsburg Univ., Pittsburgh, PA, USA

fYear

1993

fDate

1993

Firstpage

137

Lastpage

139

Abstract

The back-propagation neural network is utilized to classify sleep stages in humans. A single-channel EEG is segmented into equally spaced intervals, each interval corresponds to one-minute in time. Measurements of the time, frequency, and energy characteristics are carried out in each interval to construct the sleep pattern vector. An adaptive training algorithm is utilized to accelerate the training process. This neural network is useful for various neurological studies and clinical diagnoses.

Keywords

backpropagation; electroencephalography; medical signal processing; neural nets; 1 min; adaptive training algorithm; automatic classification; back-propagation neural network; clinical diagnoses; energy characteristics; frequency characteristics; neurological studies; single-channel EEG; sleep stages; time characteristics; Artificial neural networks; Electroencephalography; Energy measurement; Frequency estimation; Frequency measurement; Humans; Inspection; Neural networks; Sleep; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering Conference, 1993., Proceedings of the Twelfth Southern

Conference_Location

New Orleans, LA, USA

Print_ISBN

0-7803-0976-6

Type

conf

DOI

10.1109/SBEC.1993.247388

Filename

247388