A fuzzy neural network approach for automatic K-complex detection in sleep EEG signal

Abstract The study of sleep stages and the associated signals have emerged as a very important parameter to identify the neurological disorders and test of mental activities nowadays. Electroencephalogram (EEG) is an electrophysiological method for monitoring, managing, and diagnosing the mental disorders or neurological problems. The EEG signals are highly transient and nonlinear in nature. It varies with the mental conditions. In the sleep state, a non-stationary wave generates with comparatively higher peaks is known as K-complex. The K-complex is a kind of transient wave which can be seen in the NREM stage II sleep. The main difficulty behind the design of the automated K-complex detection system is a nonlinear and dynamic characterization of it. The other difficulty for the system design is the very much similar behaviour of K-complex to other EEG wave. To overcome these problems, in this paper we are giving the detailed description for developing an automatic K-complex detector using fuzzy neural network approach. In this method, fuzzy C-means algorithm is utilized for the rough and rapid recognition of K-complex and the neural network classifier does the exact evaluation on the detected K-complex. One more fast computing Back Proportion algorithm is used for train the network in this work. This technique of detection of K-complex with a well-known pattern present in sleep EEG is a fuzzy neural based software solution in the field of biomedical signal processing.

[1]  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.

[2]  Mohammad Mikaeili,et al.  K-complex identification in sleep EEG using MELM-GRBF classifier , 2014, 2014 21th Iranian Conference on Biomedical Engineering (ICBME).

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

[4]  C Strungaru,et al.  Neural Network for Sleep EEG K-Complex Detection , 1998, Biomedizinische Technik. Biomedical engineering.

[5]  Verónica Bolón-Canedo,et al.  A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..

[6]  T. Dutoit,et al.  Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[7]  M. Kryger,et al.  Principles and Practice of Sleep Medicine , 1989 .

[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]  Verónica Bolón-Canedo,et al.  A comparison of performance of K-complex classification methods using feature selection , 2016, Inf. Sci..

[10]  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 .

[11]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[12]  Teofilo L. Lee-Chiong,et al.  Sleep: a comprehensive handbook , 2006 .

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

[14]  Naohiro Ishii,et al.  Detection of the K-Complex Using a New Method of Recognizing Waveform Based on the Discrete Wavelet Transform , 1995, IEICE Trans. Inf. Syst..

[15]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[16]  AcharyaU. Rajendra,et al.  Automated EEG analysis of epilepsy , 2013 .

[17]  B.H. Jansen,et al.  Knowledge-based approach to sleep EEG analysis-a feasibility study , 1989, IEEE Transactions on Biomedical Engineering.

[18]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[19]  R Lengelle,et al.  Joint time and time-frequency optimal detection of K-complexes in sleep EEG. , 1998, Computers and biomedical research, an international journal.

[20]  F. H. Lopes da Silva,et al.  A model-based detector of vertex waves and K complexes in sleep electroencephalogram. , 1991, Electroencephalography and clinical neurophysiology.

[21]  Isaac Fernández-Varela,et al.  A Comparison of Performance of Sleep Spindle Classification Methods Using Wavelets , 2016 .

[22]  T. lee-chiong,et al.  Fundamentals of sleep technology , 2012 .

[23]  J. R. Smith,et al.  Automatic detection of the K-complex in sleep electroencephalograms. , 1970, IEEE transactions on bio-medical engineering.

[24]  A. Rani,et al.  Application of adaptive Savitzky–Golay filter for EEG signal processing☆ , 2016 .

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

[26]  Peter Vamplew,et al.  DETECTING K-COMPLEXES FOR SLEEP STAGE IDENTIFICATION USING NONSMOOTH OPTIMIZATION , 2011, The ANZIAM Journal.

[27]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[28]  Diego Álvarez-Estévez,et al.  Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome , 2009, Expert Syst. Appl..

[29]  I. Bankman,et al.  Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks , 1992, IEEE Transactions on Biomedical Engineering.

[30]  B.H. Jansen.  Artificial neural nets for K-complex detection , 1990, IEEE Engineering in Medicine and Biology Magazine.

[31]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[33]  Péter Halász,et al.  K-complex, a reactive EEG graphoelement of NREM sleep: an old chap in a new garment. , 2005, Sleep medicine reviews.

[34]  Abdennaceur Kachouri,et al.  Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis , 2015, Front. Hum. Neurosci..

[35]  Aykut Erdamar,et al.  A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG , 2012, Expert Syst. Appl..

[36]  Haoxiang Wang,et al.  An approach for hyperspectral image classification by optimizing SVM using self organizing map , 2017, J. Comput. Sci..

[37]  Jie Yang,et al.  Kernelized support vector machine with deep learning: An efficient approach for extreme multiclass dataset , 2017, Pattern Recognit. Lett..

[38]  Amparo Alonso-Betanzos,et al.  A Global Optimum Approach for One-Layer Neural Networks , 2002, Neural Computation.

[39]  Gang Li,et al.  K-Complex Detection Using a Hybrid-Synergic Machine Learning Method , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[40]  Amparo Alonso-Betanzos,et al.  A new convex objective function for the supervised learning of single-layer neural networks , 2010, Pattern Recognit..

[41]  D. Sauter,et al.  Recognition of K-complex in sleep EEG using a fuzzy C-means algorithm , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[42]  Amparo Alonso-Betanzos,et al.  Reducing dimensionality in a database of sleep EEG arousals , 2011, Expert Syst. Appl..

[43]  Verónica Bolón-Canedo,et al.  A review of feature selection methods on synthetic data , 2013, Knowledge and Information Systems.

[44]  Huan Liu,et al.  Consistency-based search in feature selection , 2003, Artif. Intell..