Sleep stage classification using single-channel EOG

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.

[1]  Sheng-Fu Liang,et al.  Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models , 2012, IEEE Transactions on Instrumentation and Measurement.

[2]  S V Selishchev,et al.  Classification of human sleep stages based on EEG processing using hidden Markov models , 2007, Meditsinskaia tekhnika.

[3]  Marina Ronzhina,et al.  Sleep scoring using artificial neural networks. , 2012, Sleep medicine reviews.

[4]  Jussi Toppila,et al.  Electro-oculography-based detection of sleep-wake in sleep apnea patients , 2015, Sleep and Breathing.

[5]  Yen-Chen Liu,et al.  Development of an EOG-Based Automatic Sleep-Monitoring Eye Mask , 2015, IEEE Transactions on Instrumentation and Measurement.

[6]  J. Allan Hobson,et al.  A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects: A. Rechtschaffen and A. Kales (Editors). (Public Health Service, U.S. Government Printing Office, Washington, D.C., 1968, 58 p., $4.00) , 1969 .

[7]  A. Rechtschaffen,et al.  A manual of standardized terminology, technique and scoring system for sleep stages of human subjects , 1968 .

[8]  Mohammed Imamul Hassan Bhuiyan,et al.  On the classification of sleep states by means of statistical and spectral features from single channel Electroencephalogram , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[9]  Yike Guo,et al.  Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders , 2015, Annals of Biomedical Engineering.

[10]  Julie A. E. Christensen,et al.  A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[12]  A. Hassan,et al.  A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features , 2016, Journal of Neuroscience Methods.

[13]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Bin Xia,et al.  Single Electrooculogram Channel-Based Sleep Stage Classification , 2016 .

[15]  Gerhard Tröster,et al.  Wearable EOG goggles: eye-based interaction in everyday environments , 2009, CHI Extended Abstracts.

[16]  Yan Li,et al.  Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal , 2014, IEEE Journal of Biomedical and Health Informatics.

[17]  K. Müller,et al.  Automatic sleep stage classification using two-channel electro-oculography , 2007, Journal of Neuroscience Methods.

[18]  Yen-Chen Liu,et al.  An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control , 2014, PhyCS.

[19]  D. Rapoport,et al.  Interobserver agreement among sleep scorers from different centers in a large dataset. , 2000, Sleep.

[20]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[21]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[22]  Jingyi Wang,et al.  Electrooculogram based sleep stage classification using deep belief network , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[23]  Urbano Nunes,et al.  ISRUC-Sleep: A comprehensive public dataset for sleep researchers , 2016, Comput. Methods Programs Biomed..

[24]  Masaaki Fukumoto,et al.  Full-time wearable headphone-type gaze detector , 2006, CHI Extended Abstracts.

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  Sheng-Fu Liang,et al.  A rule-based automatic sleep staging method , 2012, Journal of Neuroscience Methods.

[27]  Hamed Azami,et al.  Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.

[28]  Mohammed Imamul Hassan Bhuiyan,et al.  Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting , 2017, Comput. Methods Programs Biomed..

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  Hamed Azami,et al.  Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals , 2016, IEEE Transactions on Biomedical Engineering.

[31]  J. Mattout,et al.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals. , 2007, Sleep.

[32]  T. Penzel,et al.  Computer based sleep recording and analysis. , 2000, Sleep medicine reviews.

[33]  Cabir Vural,et al.  Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis , 2010, Journal of Medical Systems.