Genetic fuzzy classifier for sleep stage identification.
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.
Jo
Han G
HG
Department of Medical Engineering, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-gu, Seoul, South Korea.
Park
Jin Y
JY
Lee
Chung K
CK
An
Suk K
SK
Yoo
Sun K
SK
D000328:Adult; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D005583:Fourier Analysis; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008957:Models, Genetic; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*