Title :
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
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;
Conference_Titel :
Biomedical Engineering Conference, 1993., Proceedings of the Twelfth Southern
Conference_Location :
New Orleans, LA, USA
Print_ISBN :
0-7803-0976-6
DOI :
10.1109/SBEC.1993.247388
