Genetic fuzzy classifier for sleep stage identification

Authors:
Han G. Jo;Jin Y. Park;Chung K. Lee;Suk K. An;Sun K. Yoo
Affiliations:
Department of Medical Engineering, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-gu, Seoul, South Korea;Section of Affect and Neuroscience, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea and Department of Psychiatry, Gongju National Hospital, C ...;Department of Medical Engineering, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-gu, Seoul, South Korea;Department of Psychiatry, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-gu, Seoul, South Korea and Section of Affect and Neuroscience, Institute of Behavioral Science in Medicin ...;Department of Medical Engineering, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-gu, Seoul, South Korea and Brain Korea 21 Project for Yonsei University College of Medicine, 250 ...
Venue:
Computers in Biology and Medicine
Year:
2010

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Abstract

Soft-computing techniques are commonly used to detect medical phenomena and help with clinical diagnoses and treatment. In this work, we propose a design for a computerized sleep scoring method, which is based on a fuzzy classifier and a genetic algorithm (GA). We design the fuzzy classifier based on the GA using a single electroencephalogram (EEG) signal that detects differences in spectral features. Polysomnography was performed on four healthy young adults (males with a mean age of 27.5 years). The sleep classifier was designed using a sleep record and tested on the sleep records of the subjects. Our results show that the genetic fuzzy classifier (GFC) agreed with visual sleep staging approximately 84.6% of the time in detection of wakefulness (WA), shallow sleep (SS), deep sleep (DS), and rapid eye movement (REM) stages.