A New Method for Automatic Sleep Stage Classification

Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.

[1]  Tohru Nitta,et al.  Orthogonality of Decision Boundaries in Complex-Valued Neural Networks , 2004, Neural Computation.

[2]  Akira Hirose,et al.  Plastic mine detecting radar system using complex-valued self-organizing map that deals with multiple-frequency interferometric images , 2004, Neural Networks.

[3]  James M. Keller,et al.  Sleep stage recognition using respiration signal , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Seral Özsen,et al.  Classification of sleep stages using class-dependent sequential feature selection and artificial neural network , 2012, Neural Computing and Applications.

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

[6]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Yann LeCun,et al.  Fast Training of Convolutional Networks through FFTs , 2013, ICLR.

[8]  Thomas Philip Runarsson,et al.  Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[9]  Tarik Al-Ani,et al.  Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: A comparative study using short and standard epoch lengths , 2013, Comput. Methods Programs Biomed..

[10]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[11]  Sebastian Stober,et al.  Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings , 2014, NIPS.

[12]  Wenyao Xu,et al.  SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[13]  Kemal Polat,et al.  Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting , 2010, Expert Syst. Appl..

[14]  A. M. Torres,et al.  Eye Movement Artefact Suppression Using Volterra Filter for Electroencephalography Signals , 2015 .

[15]  Murata Noboru,et al.  Predicting sleep stages based on time-dependent hidden Markov model , 2006 .

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

[17]  David Fernandes,et al.  SAR image classification using a neural classifier based on Fisher criterion , 2002, VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings..

[18]  Masafumi Hagiwara,et al.  Memorization of Melodies Using Complex-Valued Recurrent Neural Network , 2003 .

[19]  Yann LeCun,et al.  Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation , 2007, AAAI.

[20]  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.

[21]  Shing-Tai Pan,et al.  Application of Genetic Algorithm and Fuzzy Vector Quantization on EEG-based automatic sleep staging by using Hidden Markov Model , 2014, 2014 International Conference on Machine Learning and Cybernetics.

[22]  Amy Loutfi,et al.  Sleep Stage Classification Using Unsupervised Feature Learning , 2012, Adv. Artif. Neural Syst..

[23]  V. D. Mytri,et al.  Sleep stages classification using wavelettransform & neural network , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[24]  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.

[25]  Patrick C. Fischer,et al.  Efficient Procedures for Using Matrix Algorithms , 1974, ICALP.

[26]  B. Weiss,et al.  Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages , 2011, Brain Research Bulletin.

[27]  Olaf Hellwich,et al.  Complex-Valued Convolutional Neural Networks for Object Detection in PolSAR data , 2010 .

[28]  Markus Bläser,et al.  Fast Matrix Multiplication , 2013, Theory Comput..

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

[30]  Yasuaki Kuroe.  A Model of Complex-Valued Associative Memories and Its Dynamics , 2003 .

[31]  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.

[32]  Reza Boostani,et al.  A comparative review on sleep stage classification methods in patients and healthy individuals , 2017, Comput. Methods Programs Biomed..

[33]  C. Held,et al.  Expert-system classification of sleep/waking states in infants , 1999, Medical & Biological Engineering & Computing.

[34]  Georg Dorffner,et al.  A reliable probabilistic sleep stager based on a single EEG signal , 2005, Artif. Intell. Medicine.

[35]  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.

[36]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[37]  Tohru Nitta.  The Computational Power of Complex-Valued Neuron , 2003, ICANN.

[38]  Roman Genov,et al.  Electronic Sleep Stage Classifiers: A Survey and VLSI Design Methodology , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[39]  Deniz Tuncel,et al.  Automatic classification of sleep stages with artificial neural networks according to visual scoring rules , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

[40]  Yike Guo,et al.  Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks , 2016, ArXiv.

[41]  Wenbin Shi,et al.  Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. , 2018, Sleep medicine reviews.

[42]  Olaf Hellwich,et al.  CLASSIFICATION OF POLARIMETRIC SAR DATA BY COMPLEX VALUED NEURAL NETWORKS , 2009 .

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

[44]  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.

[45]  Tülay Adali,et al.  Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing , 2002, J. VLSI Signal Process..

[46]  Dario Floreano,et al.  Sleep and Wake Classification With ECG and Respiratory Effort Signals , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[47]  Hubert Cecotti,et al.  A time-frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses , 2011, Pattern Recognit. Lett..

[48]  Thierry Dutoit,et al.  Automatic sleep spindles detection — Overview and development of a standard proposal assessment method , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[50]  Do-Un Jeong,et al.  Hybrid neural-network and rule-based expert system for automatic sleep stage scoring , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

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

[52]  Yosuke Kurihara,et al.  Sleep-Stage Decision Algorithm by Using Heartbeat and Body-Movement Signals , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[53]  Narasimhan Sundararajan,et al.  Fast learning complex-valued classifiers for real-valued classification problems , 2013, Int. J. Mach. Learn. Cybern..

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

[55]  B. Koley,et al.  An ensemble system for automatic sleep stage classification using single channel EEG signal , 2012, Comput. Biol. Medicine.

[56]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[57]  Suzanne Lesecq,et al.  Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging , 2011, Comput. Biol. Medicine.

[58]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[60]  ALEXSANDRO MACHADO JACOB,et al.  SAR IMAGE CLASSIFICATION USING SUPERVISED NEURAL CLASSIFIERS , 2003 .

[61]  G Dumermuth,et al.  Spectral analysis of EEG activity in different sleep stages in normal adults. , 1972, European neurology.

[62]  Bharti Kamlesh Kanoje,et al.  Automatic Sleep Stage Detection of an EEG Signal Using an Ensemble Method , 2014 .

[63]  Narasimhan Sundararajan,et al.  Supervised Learning with Complex-valued Neural Networks , 2012, Studies in Computational Intelligence.

[64]  Necmettin Sezgin,et al.  Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG , 2010, Journal of Medical Systems.

[65]  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..

[66]  Ioanna Chouvarda,et al.  Assessment of the EEG complexity during activations from sleep , 2011, Comput. Methods Programs Biomed..

[67]  Tohru Nitta,et al.  An Extension of the Back-Propagation Algorithm to Complex Numbers , 1997, Neural Networks.

[68]  Miad Faezipour,et al.  Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation , 2016, Entropy.

[69]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

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

[71]  Elena Hernández-Pereira,et al.  A method for the automatic analysis of the sleep macrostructure in continuum , 2013, Expert Syst. Appl..

[72]  Ya-Ti Peng,et al.  Multimodality Sensor System for Long-Term Sleep Quality Monitoring , 2007, IEEE Transactions on Biomedical Circuits and Systems.

[73]  Jake Bouvrie,et al.  Notes on Convolutional Neural Networks , 2006 .

[74]  Igor N. Aizenberg,et al.  Complex-Valued Neural Networks with Multi-Valued Neurons , 2011, Studies in Computational Intelligence.

[75]  Yann LeCun,et al.  Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

[76]  Mohamad Ivan Fanany,et al.  Combining Generative and Discriminative Neural Networks for Sleep Stages Classification , 2016, ArXiv.

[77]  Hau-Tieng Wu,et al.  Assess Sleep Stage by Modern Signal Processing Techniques , 2014, IEEE Transactions on Biomedical Engineering.

[78]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[79]  Dong Yu,et al.  Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP] , 2011, IEEE Signal Processing Magazine.

[80]  Anna Krakovská,et al.  Automatic sleep scoring: A search for an optimal combination of measures , 2011, Artif. Intell. Medicine.

[81]  Suzanne Lesecq,et al.  Feature selection for sleep/wake stages classification using data driven methods , 2007, Biomed. Signal Process. Control..

[82]  Isak Gath,et al.  Unsupervised classification and adaptive definition of sleep patterns , 1994, Pattern Recognit. Lett..

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

[84]  Dong Yu,et al.  Deep Learning and Its Applications to Signal and Information Processing , 2011 .

[85]  Kazuyuki Murase,et al.  Single-layered complex-valued neural network for real-valued classification problems , 2009, Neurocomputing.

[86]  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.