A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

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

[2]  H. Adeli,et al.  Automated seizure prediction , 2018, Epilepsy & Behavior.

[3]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[4]  U. Rajendra Acharya,et al.  A deep convolutional neural network model for automated identification of abnormal EEG signals , 2018, Neural Computing and Applications.

[5]  Alexandros T. Tzallas,et al.  EEG-Based Automatic Sleep Stage Classification , 2018, Biomedical Journal of Scientific & Technical Research.

[6]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

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

[8]  U. Rajendra Acharya,et al.  Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats , 2018, Comput. Biol. Medicine.

[9]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[10]  Özal Yildirim,et al.  A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification , 2018, Comput. Biol. Medicine.

[11]  Saeid Sanei,et al.  Deep Neural Architectures for Mapping Scalp to Intracranial EEG , 2018, Int. J. Neural Syst..

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

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

[14]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[15]  A. Avidan,et al.  Review of sleep disorders. , 1990, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[16]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[17]  Farhad Faradji,et al.  A Novel Multi-Class EEG-Based Sleep Stage Classification System , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[19]  J. Röschke,et al.  Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. , 1996, Electroencephalography and clinical neurophysiology.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[22]  Chin-Teng Lin,et al.  Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels , 2014, Front. Neurosci..

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

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

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

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

[27]  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.

[28]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.

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

[30]  U. Rajendra Acharya,et al.  Application of deep transfer learning for automated brain abnormality classification using MR images , 2019, Cognitive Systems Research.

[31]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[32]  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.

[33]  Musa Peker,et al.  A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms , 2014, Journal of Medical Systems.

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

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

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

[37]  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.

[38]  Sabine Van Huffel,et al.  An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation , 2017, Int. J. Neural Syst..

[39]  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.

[40]  Mohamad Ivan Fanany,et al.  Fast Convolutional Method for Automatic Sleep Stage Classification , 2018, Healthcare informatics research.

[41]  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.

[42]  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..

[43]  Danilo P. Mandic,et al.  Complexity science for sleep stage classification from EEG , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

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

[45]  Mehrdad Nourani,et al.  Multi-Biosignal Analysis for Epileptic Seizure Monitoring , 2017, Int. J. Neural Syst..

[46]  Esther Rodríguez-Villegas,et al.  Automatic sleep staging using state machine-controlled decision trees , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[47]  Tom Chau,et al.  Online EEG Classification of Covert Speech for Brain-Computer Interfacing , 2017, Int. J. Neural Syst..

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

[49]  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 .

[50]  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..

[51]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[52]  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.