Optimized automatic sleep stage classification using the normalized mutual information feature selection (NMIFS) method

Sleep is a very important physiological phenomenon for recovery of physical and mental fatigue. Recently, there has been a lot of interest in the quality of sleep and the research is actively under way. In particular, it is important to have a repetitive and regular sleep cycle for good sleep. However, it takes a lot of time to determine sleep stages using physiological signals by experts. In this study, we constructed an optimized classifier based on normalized mutual information feature selection (NMIFS) and kernel based extreme learning machine (K-ELM), and total 4 sleep stages (Awake, weak sleep (stage1+stage2), deep sleep(stage3+stage4) and rapid eye movement (REM)) were automatically classified. As a results, the average of the accuracy obtained by proposed method (NMIFS+K-ELM) is 2~3% higher than that of simple method (K-ELM).