Abstract
Sleep stage classification is an important task for the timely diagnosis of sleep disorders and sleep-related studies. In this paper, automatic scoring of sleep stages using Electrooculogram (EOG) is presented. Single channel EOG signals are analyzed in Discrete Wavelet Transform (DWT) domain employing various statistical features such as Spectral Entropy, Moment-based Measures, Refined Composite Multiscale Dispersion Entropy (RCMDE) and Autoregressive (AR) Model Coefficients. The discriminating ability of the features is studied using the One Way Analysis of Variance (ANOVA) and box plots. A feature reduction algorithm based on Neighborhood Component Analysis is used to reduce the model complexity and select the features with highest discriminating abilities. Random Under-Sampling Boosting (RUSBoost), Random Forest (RF) and Support Vector Machine (SVM) are employed to classify various sleep stages for 2–6 stage classification problem. Performance of the proposed method is studied using three publicly available databases, the Sleep-EDF, Sleep-EDFX and ISRUC-Sleep databases consisting of 8, 20 and 10 subjects respectively. The proposed method outperforms the state-of-the-art EOG based techniques in accuracy. In addition, its performance is shown to be on par or better than those of various single channel EEG based methods. An important limitation of existing sleep detection methods is the low accuracy of the S1 sleep stage classification for which the proposed method using the RUSBoost classifier gives a superior accuracy as compared to those of EOG and EEG based techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.