Sleep EEG analysis utilizing inter-channel covariance matrices

Abstract Background Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep. New method Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen-vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%). Comparison with existing method The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion The performances of RG-TS and E1 features reveal that the changes in inter-dependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages.

[1]  Clifton Frilot,et al.  An original method for staging sleep based on dynamical analysis of a single EEG signal , 2018, Journal of Neuroscience Methods.

[2]  Yu Ma,et al.  Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement , 2019, Expert Syst. Appl..

[3]  Junming Zhang,et al.  A New Method for Automatic Sleep Stage Classification , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[4]  K. Ramar,et al.  An Intelligent Sleep Apnea Classification System Based on EEG Signals , 2019, Journal of Medical Systems.

[5]  Laurent Vercueil,et al.  A convolutional neural network for sleep stage scoring from raw single-channel EEG , 2018, Biomed. Signal Process. Control..

[6]  M. Czisch,et al.  Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study. , 2006, Brain : a journal of neurology.

[7]  Mohammed Imamul Hassan Bhuiyan,et al.  Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating , 2016, Biomed. Signal Process. Control..

[8]  Javad Frounchi,et al.  Wavelet-based emotion recognition system using EEG signal , 2017, Neural Computing and Applications.

[9]  Stanislas Chambon,et al.  A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  M. Carskadon,et al.  Chapter 2 - Normal Human Sleep : An Overview , 2005 .

[11]  C. Stam,et al.  In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer’s disease , 2019, Clinical Neurophysiology.

[12]  Yong Zhang,et al.  Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition , 2016, Neural Processing Letters.

[13]  Alexandre Barachant,et al.  Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review , 2017 .

[14]  U. Rajendra Acharya,et al.  A review of automated sleep stage scoring based on physiological signals for the new millennia , 2019, Comput. Methods Programs Biomed..

[15]  A. Maki,et al.  Activation of visual cortex in REM sleep measured by 24‐channel NIRS imaging , 2001, Psychiatry and clinical neurosciences.

[16]  Joel E. W. Koh,et al.  Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection , 2015, European Neurology.

[17]  U. Rajendra Acharya,et al.  Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework , 2018 .

[18]  U. Rajendra Acharya,et al.  An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank , 2018, Comput. Biol. Medicine.

[19]  Mohammed Imamul Hassan Bhuiyan,et al.  An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting , 2017, Neurocomputing.

[20]  Ali Motie Nasrabadi,et al.  A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal , 2018, Expert Syst. Appl..

[21]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[22]  Chao Wu,et al.  DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  P. Achermann,et al.  In human non-REM sleep, more slow-wave activity leads to less blood flow in the prefrontal cortex , 2017, Scientific Reports.

[24]  Abdulhamit Subasi,et al.  Ensemble SVM Method for Automatic Sleep Stage Classification , 2018, IEEE Transactions on Instrumentation and Measurement.

[25]  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..

[26]  C. Degueldre,et al.  Functional neuroanatomy of human rapid-eye-movement sleep and dreaming , 1996, Nature.

[27]  C. Kelly,et al.  Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms , 2020, Clinical Neurophysiology.

[28]  Abdulhamit Subasi,et al.  A decision support system for automated identification of sleep stages from single-channel EEG signals , 2017, Knowl. Based Syst..

[29]  Christian Jutten,et al.  Classification of covariance matrices using a Riemannian-based kernel for BCI applications , 2013, Neurocomputing.

[30]  G Deco,et al.  Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep , 2019, Nature Communications.

[31]  Priyanka S. Ghare,et al.  Human emotion recognition using non linear and non stationary EEG signal , 2016, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).

[32]  Dean Cvetkovic,et al.  EEG biomarker of Sleep Apnoea using discrete wavelet transform and approximate entropy , 2017, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[33]  Robert W McCarley,et al.  Neurobiology of REM and NREM sleep. , 2007, Sleep medicine.

[34]  U. Rajendra Acharya,et al.  SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach , 2019, PloS one.

[35]  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.

[36]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[38]  J. Buhmann,et al.  Automatic Human Sleep Stage Scoring Using Deep Neural Networks , 2018, Front. Neurosci..

[39]  Pengjian Shang,et al.  A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining , 2017, Commun. Nonlinear Sci. Numer. Simul..

[40]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[41]  A. Braun,et al.  Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study. , 1997, Brain : a journal of neurology.

[42]  Mohammed Imamul Hassan Bhuiyan,et al.  Automatic sleep scoring using statistical features in the EMD domain and ensemble methods , 2016 .

[43]  U. Rajendra Acharya,et al.  Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals , 2019, Comput. Biol. Medicine.

[44]  Mohammed Imamul Hassan Bhuiyan,et al.  Sleep stage classification using single-channel EOG , 2018, Comput. Biol. Medicine.

[45]  Mohammad Sadegh Helfroush,et al.  An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model , 2019, Journal of Neuroscience Methods.

[46]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[47]  U Rajendra Acharya,et al.  A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals , 2019, International journal of environmental research and public health.

[48]  C. Stam,et al.  EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis , 2018, Front. Hum. Neurosci..

[49]  José Luis Rodríguez-Sotelo,et al.  Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques , 2014, Entropy.

[50]  Yan Li,et al.  Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features , 2019, Biomed. Signal Process. Control..

[51]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[52]  Wee Ser,et al.  Deep Learning Method for Sleep Stage Classification , 2017, ICONIP.

[53]  D. Kupfer,et al.  Forebrain activation in REM sleep: an FDG PET study 1 Presented in abstract form at the Society for Neuroscience Meeting, 1996. 1 , 1997, Brain Research.

[54]  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.

[55]  R. McCarley,et al.  Control of sleep and wakefulness. , 2012, Physiological reviews.