Analysis and automatic identification of sleep stages using higher order spectra.
Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
U Rajendra
UR
Acharya
Eric Chern-Pin
EC
Chua
Kuang Chua
KC
Chua
Lim Choo
LC
Min
Toshiyo
T
Tamura
D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D004569:Electroencephalography; D019277:Entropy; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D013057:Spectrum Analysis / Q000379:methods*