Document Type : Full Research Paper


1 M.Sc. Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Assistant Professor, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

3 Ph.D Student, Department of Mechanical Engineering, Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran


Using an intelligent method to automatically detect sleep patterns in medical applications is one of the most important challenges in recent years to reduce the workload of physicians in analyzing sleep data through visual inspection. In this paper, a single-channel EEG-based algorithm is used to automatically identify sleep stages using discrete wavelet transform and a hybrid model of simulated annealing and neural network. The signal is decomposed using a discrete wavelet transform into seven levels and statistical properties of each level is calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of simulated annealing algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally the classification is performed on the validated features by a perceptron neural network with a hidden layer, which provides an average of 90% classification ccuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has higher degree of success in classifying sleep stages compared to the existing methods.


Main Subjects

[1]      A. R. Hassan and M. I. H. Bhuiyan, "Automatic sleep scoring using statistical features in the EMD domain and ensemble methods," Biocybernetics and Biomedical Engineering, vol. 36, no. 1, pp. 248-255, 2016.
[2]      S. Leistedt, M. Dumont, J.-P. Lanquart, F. Jurysta, and P. Linkowski, "Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis," Clinical neurophysiology, vol. 118, no. 4, pp. 940-950, 2007.
[3]      Y. Koshino et al., "The influence of light drowsiness on the latency and amplitude of P300," Clinical Electroencephalography, vol. 24, no. 3, pp. 110-113, 1993.
[4]      L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, "Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier," Computer methods and programs in biomedicine, vol. 108, no. 1, pp. 10-19, 2012.
[5]      T. Lajnef et al., "Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines," Journal of neuroscience methods, vol. 250, pp. 94-105, 2015.
[6]      B. Boashash and S. Ouelha, "Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study," Knowledge-Based Systems, vol. 106, pp. 38-50, 2016.
[7]      T. Penzel and R. Conradt, "Computer based sleep recording and analysis," Sleep medicine reviews, vol. 4, no. 2, pp. 131-148, 2000.
[8]      Y. Li, M.-L. Luo, and K. Li, "A multiwavelet-based time-varying model identification approach for time–frequency analysis of EEG signals," Neurocomputing, vol. 193, pp. 106-114, 2016.
[9]      A. Subasi, "A decision support system for diagnosis of neuromuscular disorders using DWT and evolutionary support vector machines," Signal, Image and Video Processing, vol. 9, no. 2, pp. 399-408, 2015.
[10]      A. R. Hassan and M. I. H. Bhuiyan, "Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting," Computer methods and programs in biomedicine, vol. 140, pp. 201-210, 2017.
[11]      G. Zhu, Y. Li, and P. P. Wen, "Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal," IEEE journal of biomedical and health informatics, vol. 18, no. 6, pp. 1813-1821, 2014.
[12]      M. Ronzhina, T. Potocnak, O. Janousek, J. Kolarova, M. Novakova, and I. Provaznik, "Spectral and higher-order statistical analysis of the ECG: Application to the study of ischemia in rabbit isolated hearts," IEEE Transactions on Information Theory vol.49, no. 6, pp. 645-648: 2012
[13]      S.F. Liang, C.E. Kuo, Y.H. Hu and Y.S. Cheng, "A rule-based automatic sleep staging method," Journal of neuroscience methods, vol. 205, no. 1, pp. 169-176, 2012.
[14]      A. Krakovská and K. Mezeiová, "Automatic sleep scoring: A search for an optimal combination of measures," Artificial intelligence in medicine, vol. 53, no. 1, pp. 25-33, 2011.
[15]      T. L. da Silveira, A. J. Kozakevicius, and C. R. Rodrigues, "Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain," Medical & biological engineering & computing, vol. 55, no. 2, pp. 343-352, 2017.
[16]      M. Peker, "A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform," Computer methods and programs in biomedicine, vol. 129, pp. 203-216, 2016.
[17]      S.F. Liang, C.E. Kuo, Y.H. Hu, Y.H. Pan and Y.H. Wang, "Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models," IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 6, pp. 1649-1657, 2012.
[18]      T. Kayikcioglu, M. Maleki, and K. Eroglu, "Fast and accurate PLS-based classification of EEG sleep using single channel data," Expert Systems with Applications, vol. 42, no. 21, pp. 7825-7830, 2015.
[19]      C. Vural and M. Yildiz, "Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis," Journal of medical systems, vol. 34, no. 1, pp. 83-89, 2010.
[20]      L. Doroshenkov, V. Konyshev, and S. Selishchev, "Classification of human sleep stages based on EEG processing using hidden Markov models," Biomedical Engineering, vol. 41, no. 1, pp. 25-28, 2007.
[21]      C. S. Burrus, R. A. Gopinath, H. Guo, J. E. Odegard, and I. W. Selesnick, Introduction to wavelets and wavelet transforms: a primer. Prentice hall New Jersey, 1998.
[22]      A. S. Al-Fahoum and A. A. Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains," ISRN neuroscience, vol. 2014, 2014.
[23]      P. J. Van Laarhoven and E. H. Aarts, "Simulated annealing," in Simulated annealing: Theory and applications: Springer, 1987, pp. 7-15.
[24]      H. Pourghassem and S. Daneshvar, "A framework for medical image retrieval using merging-based classification with dependency probability-based relevance feedback," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, no. 3, pp. 882-896, 2013.
[25]      A. B. Sankar, J. A. V. Selvi, D. Kumar, and K. S. Lakshmi, "Effective enhancement of classification of respiratory states using feed forward back propagation neural networks," Sadhana, vol. 38, no. 3, pp. 377-395, 2013.
[26]      A. Rechtschaffen, "Techniques and Scoring Systems for Sleep Stages of Human Subjects," A Manual of Standardized Terminology, 1978.
[27]      R. W. Homan, J. Herman, and P. Purdy, "Cerebral location of international 10–20 system electrode placement," Electroencephalography and clinical neurophysiology, vol. 66, no. 4, pp. 376-382, 1987.
[28]      Y. Liao and V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection1," Computers & security, vol. 21, no. 5, pp. 439-448, 2002.
[29]      K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, 2006.
[30]      A. J. Izenman, "Linear discriminant analysis," in Modern multivariate statistical techniques: Springer, 2013, pp. 237-280.
[31]      C. Berthomier et al., "Automatic analysis of single-channel sleep EEG: validation in healthy individuals," Sleep, vol. 30, no. 11, pp. 1587-1595, 2007.
[32]      R. Keys, "Cubic convolution interpolation for digital image processing," IEEE transactions on acoustics, speech, and signal processing, vol. 29, no. 6, pp. 1153-1160, 1981.