Chaotic analysis of Eectroencephalographic signal for sleep quality measurement

Abstract

Determination of the sleep stages is essential for sleep quality measurement, which is associated with person's daily activity, emotional disturbance, accidental risk, and illness. In this paper, we analyze electroencephalographic (EEG) signals by means of chaotic analysis methods. The EEG signals measured during sleep were modeled as the deterministic chaos system, whose representation was performed by phase space transformation. The extracted parameters from EEG signals include the delay time, the embedded dimension, the correlation dimension and the largest Lyapunov exponent. Throughout experimental observations, it has been demonstrated that extracted parameters can serve as a relative index for sleep stages identification. The difference between the light sleep and deep sleep was well discernible by all the extracted parameters, but differences within light sleep (between light sleep 1 and light sleep 2) and within deep sleep (between deep sleep 1 and deep sleep 2) were not significant. The standard error of the mean is helpful to discriminate wakefulness, and rapid eye movement (REM), in spite of their similar EEG signal patterns.