ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.

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

[2]  Mustafa Poyraz,et al.  Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction , 2009, Expert Syst. Appl..

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

[4]  Richard Stephenson,et al.  Design and validation of a computer-based sleep-scoring algorithm , 2004, Journal of Neuroscience Methods.

[5]  Georg Dorffner,et al.  An automatic, continuous and probabilistic sleep stager based on a hidden markov model , 2002, Appl. Artif. Intell..

[6]  A. Muzet,et al.  Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. , 1996, Sleep.

[7]  Gert Pfurtscheller,et al.  AI-based approach to automatic sleep classification , 2004, Biological Cybernetics.

[8]  L. Jackson.  Digital filters and signal processing , 1985 .

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  Georg Dorffner,et al.  Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[11]  R. Ferri,et al.  Relationship between Delta, Sigma, Beta, and Gamma EEG bands at REM sleep onset and REM sleep end , 2001, Clinical Neurophysiology.

[12]  Alfredo Álvarez,et al.  Sleep stage classification using fuzzy sets and machine learning techniques , 2004, Neurocomputing.

[13]  Necmettin Sezgin,et al.  The ANN-based computing of drowsy level , 2009, Expert Syst. Appl..

[14]  V. Pohl,et al.  Neuro-fuzzy recognition of K-complexes in sleep EEG signals , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[15]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[16]  Alpo Värri,et al.  Computer program for automated sleep depth estimation , 2006, Comput. Methods Programs Biomed..

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

[18]  A. Rechtschaffen.  A manual of standardized terminology, techniques and scoring system for sleep of human subjects , 1968 .

[19]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[20]  G. Dumermuth,et al.  EEG-based multivariate statistical analysis of sleep stages. , 1984, Neuropsychobiology.

[21]  J P Macher,et al.  Neural network model: application to automatic analysis of human sleep. , 1993, Computers and biomedical research, an international journal.

[22]  Hae-Jeong Park,et al.  Automated Sleep Stage Scoring Using Hybrid Rule- and Case-Based Reasoning , 2000, Comput. Biomed. Res..

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

[24]  Tong Zhang,et al.  A multistage, multimethod approach for automatic detection and classification of epileptiform EEG , 2002, IEEE Transactions on Biomedical Engineering.

[25]  Luay Fraiwan,et al.  Automatic Sleep Stage Scoring with Wavelet Packets Based on Single EEG Recording , 2009 .

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

[27]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level by using wavelet transform and artificial neural network , 2004, Journal of Neuroscience Methods.

[28]  Andrew C. N. Chen,et al.  Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. , 2002, Medical engineering & physics.

[29]  C. Eswaran,et al.  Automated Detection of Anesthetic Depth Levels Using Chaotic Features with Artificial Neural Networks , 2007, Journal of Medical Systems.

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

[31]  T. Shimada,et al.  Sleep stage diagnosis system with neural network analysis , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[32]  L. Tarassenko,et al.  Analysis of the sleep EEG using a multilayer network with spatial organisation , 1992 .

[33]  Kristína Susmáková,et al.  Discrimination ability of individual measures used in sleep stages classification , 2008, Artif. Intell. Medicine.

[34]  Nurettin Acir,et al.  Automatic recognition of sleep spindles in EEG by using artificial neural networks , 2004, Expert Syst. Appl..

[35]  Kajiro Watanabe,et al.  Noncontact method for sleep stage estimation , 2004, IEEE Transactions on Biomedical Engineering.

[36]  B. Hofmann-Wellenhof,et al.  Introduction to spectral analysis , 1986 .

[37]  Masaki Kobayashi,et al.  Automatic sleep stage scoring based on waveform recognition method and decision-tree learning , 2002, Systems and Computers in Japan.