An Accurate Sleep Stages Classification Method Based on State Space Model

The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a state-space based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.

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

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

[3]  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.

[4]  D. Tempesta,et al.  Sleep and emotional processing. , 2017, Sleep medicine reviews.

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

[6]  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.

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

[8]  Kenneth P. Camilleri,et al.  Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models , 2014, Biomed. Signal Process. Control..

[9]  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.

[10]  Mohamed Moshrefi-Torbati,et al.  Signal processing techniques applied to human sleep EEG signals - A review , 2014, Biomed. Signal Process. Control..

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

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

[13]  U. Rajendra Acharya,et al.  Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..

[14]  N. Sriraam,et al.  Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection , 2016, Australasian Physical & Engineering Sciences in Medicine.

[15]  Patrícia Pinto,et al.  Sleep quality components and mental health: Study with a non-clinical population , 2018, Psychiatry Research.

[16]  Pengjian Shang,et al.  Characterizing time series by extended complexity-entropy curves based on Tsallis, Rényi, and power spectral entropy. , 2018, Chaos.

[17]  Sule Yücelbas,et al.  A novel system for automatic detection of K-complexes in sleep EEG , 2017, Neural Computing and Applications.

[18]  P. Van Overschee,et al.  Subspace algorithms for the stochastic identification problem , 1991 .

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

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

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

[22]  Ales Procházka,et al.  Sleep scoring using polysomnography data features , 2018, Signal Image Video Process..

[23]  Yu-xi Luo,et al.  K-complex morphological features in male obstructive sleep apnea-hypopnea syndrome patients , 2018, Respiratory Physiology & Neurobiology.

[24]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

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

[26]  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.

[27]  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.

[28]  E. Basch,et al.  Sleep quality in individuals diagnosed with colorectal cancer: Factors associated with sleep disturbance as patients transition off treatment , 2018, Psycho-oncology.

[29]  Vasileios Kokkinos,et al.  Semi-automatic sleep EEG scoring based on the hypnospectrogram , 2014, Journal of Neuroscience Methods.

[30]  C. Alfano,et al.  Sleep and emotion regulation: An organizing, integrative review. , 2017, Sleep medicine reviews.

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

[32]  S Kerick,et al.  Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles , 2018, Journal of neural engineering.

[33]  Yan Li,et al.  An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image , 2018, Biomed. Signal Process. Control..

[34]  Rajeev Sharma,et al.  Automatic sleep stages classification based on iterative filtering of electroencephalogram signals , 2017, Neural Computing and Applications.

[35]  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.

[36]  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..

[37]  Rakesh Ranjan,et al.  A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal , 2018, Pattern Recognit. Lett..

[38]  Manfred Deistler,et al.  Statistical analysis of novel subspace identification methods , 1996, Signal Process..

[39]  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..

[40]  Todd P. Coleman,et al.  A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea , 2017, IEEE Transactions on Biomedical Engineering.