EEG sleep stages identification based on weighted undirected complex networks

BACKGROUND AND OBJECTIVE Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. METHODS Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. RESULTS In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. CONCLUSIONS An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.

[1]  Peng Wen,et al.  Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques , 2017, IET Signal Process..

[2]  F. Ebrahimi,et al.  Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Urbano Nunes,et al.  A two-step automatic sleep stage classification method with dubious range detection , 2015, Comput. Biol. Medicine.

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

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

[6]  Alice J. Kozakevicius,et al.  Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain , 2016, Medical & Biological Engineering & Computing.

[7]  Moncef Gabbouj,et al.  Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction , 2017, Knowl. Based Syst..

[8]  Edwin R. Hancock,et al.  Graph matching and clustering using spectral partitions , 2006, Pattern Recognit..

[9]  Yan Li,et al.  Epileptogenic focus detection in intracranial EEG based on delay permutation entropy , 2013 .

[10]  Tony R. Martinez,et al.  Reduction Techniques for Instance-Based Learning Algorithms , 2000, Machine Learning.

[11]  Aniati Murni Arymurthy,et al.  Sleep stages classification using shallow classifiers , 2015, 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[12]  M. Czisch,et al.  User Research Sleep and functional imaging , 2011 .

[13]  C. Stam,et al.  Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis , 2006, Neuroscience Letters.

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

[15]  Geng Li,et al.  Effective graph classification based on topological and label attributes , 2012, Stat. Anal. Data Min..

[16]  Jinwei Sun,et al.  Sleep staging from the EEG signal using multi-domain feature extraction , 2016, Biomed. Signal Process. Control..

[17]  Ram Bilas Pachori,et al.  Automatic classification of sleep stages based on the time-frequency image of EEG signals , 2013, Comput. Methods Programs Biomed..

[18]  Mahrokh G. Shayesteh,et al.  Classification of sleep stages based on LSTAR model , 2019, Appl. Soft Comput..

[19]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[20]  Jing Zhou,et al.  Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine , 2015, Journal of Clinical Monitoring and Computing.

[21]  Aamir Saeed Malik,et al.  Automatic diagnosis of alcohol use disorder using EEG features , 2016, Knowl. Based Syst..

[22]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.

[23]  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 .

[24]  Hong Cheng,et al.  Graph Clustering Based on Structural/Attribute Similarities , 2009, Proc. VLDB Endow..

[25]  Günther Palm,et al.  Spectral graph features for the classification of graphs and graph sequences , 2014, Comput. Stat..

[26]  Lanlan Chen,et al.  Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning , 2015, Expert Syst. Appl..

[27]  Musa Peker,et al.  A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform , 2016, Comput. Methods Programs Biomed..

[28]  Yuanyuan Wang,et al.  Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds , 2019, Comput. Methods Programs Biomed..

[29]  H. Dickhaus,et al.  Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA , 2010, Methods of Information in Medicine.

[30]  Fuqiang Chen,et al.  Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers , 2016 .

[31]  Hyo Jong Lee,et al.  Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network , 2015, Int. J. Distributed Sens. Networks.

[33]  黄亚明.  PhysioBank , 2009 .

[34]  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..

[35]  Urbano Nunes,et al.  Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels , 2013, Expert Syst. Appl..

[36]  U. Rajendra Acharya,et al.  An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks , 2017, Knowl. Based Syst..

[37]  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.

[38]  U. Rajendra Acharya,et al.  Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra , 2010, Int. J. Neural Syst..

[39]  Aamir Saeed Malik,et al.  A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals , 2015, Brain Topography.

[40]  Yan Li,et al.  Classification of epileptic EEG signals based on simple random sampling and sequential feature selection , 2016, Brain Informatics.

[41]  In-Young Kim,et al.  Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis. , 2004, Medical engineering & physics.

[42]  Bülent Yener,et al.  Graph Classification via Topological and Label Attributes , 2011 .

[43]  Younès Bennani,et al.  Dendogram based SVM for multi-class classification , 2006, 28th International Conference on Information Technology Interfaces, 2006..

[44]  Robert Sabourin,et al.  “One Against One” or “One Against All”: Which One is Better for Handwriting Recognition with SVMs? , 2006 .

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

[46]  Kubra Eroglu,et al.  Fast and accurate PLS-based classification of EEG sleep using single channel data , 2015, Expert Syst. Appl..

[47]  Yan Li,et al.  Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means. , 2015, Advances in experimental medicine and biology.

[48]  Musa Peker,et al.  An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms , 2016, Neurocomputing.

[49]  Karim Jerbi,et al.  Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines , 2015, Journal of Neuroscience Methods.

[50]  Yan Li,et al.  Complex networks approach for EEG signal sleep stages classification , 2016, Expert Syst. Appl..

[51]  B Kemp,et al.  Alternative electrode placement in (automatic) sleep scoring (Fpz-Cz/Pz-Oz versus C4-A1). , 1990, Sleep.

[52]  A Värri,et al.  A simple format for exchange of digitized polygraphic recordings. , 1992, Electroencephalography and clinical neurophysiology.

[53]  Ravinesh C. Deo,et al.  Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm , 2019, Expert Syst. Appl..

[54]  Vance Gao,et al.  Multiple classifier systems for automatic sleep scoring in mice , 2016, Journal of Neuroscience Methods.

[55]  Johan A. K. Suykens,et al.  Least squares support vector machine classifiers: a large scale algorithm , 1999 .

[56]  Yan Li,et al.  EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[57]  H. Schulz,et al.  Phasic or transient? Comment on the terminology of the AASM manual for the scoring of sleep and associated events. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[58]  M S Mourtazaev,et al.  Age and gender affect different characteristics of slow waves in the sleep EEG. , 1995, Sleep.

[59]  Tianning Li,et al.  Complex networks approach for depth of anesthesia assessment , 2018 .

[60]  Serap Aydin,et al.  Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia , 2015, Journal of Medical Systems.

[61]  Xiao Liu,et al.  Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning , 2015, Journal of Neuroscience Methods.

[62]  Yu-Liang Hsu,et al.  Automatic sleep stage recurrent neural classifier using energy features of EEG signals , 2013, Neurocomputing.

[63]  D. Cvetkovic,et al.  Spectra of graphs : theory and application , 1995 .

[64]  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.

[65]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

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

[68]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[69]  Z. Mousavi,et al.  Deep convolutional neural network for classification of sleep stages from single-channel EEG signals , 2019, Journal of Neuroscience Methods.

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

[71]  S. Tobimatsu,et al.  Efficiency of a "small-world" brain network depends on consciousness level: a resting-state FMRI study. , 2014, Cerebral cortex.

[72]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[73]  Yen-Chen Liu,et al.  An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control , 2014, PhyCS.

[74]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[75]  A. Rechtschaffen.  A manual of Standardized Terminology , 1968 .

[76]  Yan Li,et al.  Clustering technique-based least square support vector machine for EEG signal classification , 2011, Comput. Methods Programs Biomed..

[77]  Rakesh Kumar Sinha,et al.  Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States , 2008, Journal of Medical Systems.