نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه مهندسی پزشکی، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

2 دانشیار، گروه مهندسی کنترل، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

چکیده

طبقه‌بندی خودکار مراحل خواب به منظور تشخیص به موقع اختلالات و مطالعات مرتبط با خواب امری ضروری است. در این مقاله یک الگوریتم مبتنی بر EEG تک‌کاناله برای شناسایی خودکار مراحل خواب با استفاده از تبدیل موجک گسسته و مدل ترکیبی الگوریتم کلونی مورچگان و شبکه‌ی عصبی مبتنی بر طبقه‌بند RUSBoost ارائه شده است. سیگنال با استفاده از تبدیل موجک گسسته به چهار سطح تجزیه‌ شده و ویژگی‌های آماری از هر یک از این سطوح استخراج شده است. جهت بهینه‌سازی و کاهش ابعاد بردارهای ویژگی، از یک مدل ترکیبی الگوریتم کلونی مورچگان و شبکه‌ی عصبی چندلایه‌ی پس‌انتشار خطا استفاده شده و سپس از آزمون ANOVA برای تایید صحت ویژگی‌های بهینه بهره گرفته شده است. طبقه‌بندی نهایی روی این ویژگی‌های بهینه شده توسط طبقه‌بند RUSBoost صورت گرفته و مشاهده شده است که به طور میانگین صحت طبقه‌بندی 2 تا 6-کلاس مراحل مختلف خواب بالای 90% بوده که نشان دهنده‌ی درصد موفقیت بالاتر روش پیشنهادی در طبقه‌بندی مراحل خواب نسبت به پژوهش‌های پیشین می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Automatic Stage Scoring of Single-Channel Sleep EEG using Discrete Wavelet Transform and a Hybrid Model of Ant Colony Optimizer and Neural Network based on RUSBoost Classifier

نویسندگان [English]

  • Sobhan Sheykhivand 1
  • Sehraneh Ghaemi 2

1 Ph.D. Student, Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Associate Professor, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

چکیده [English]

The automatic classification of sleep stages is essential for the timely detection of disorders and sleep-related studies. 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 ant colony optimizer and neural network based on RUSBoost. The signal is decomposed using a discrete wavelet transform into four levels and statistical properties of each level are calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of ant colony optimizer algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally, the classification is performed on RUSBoost, which provides an average of 90% classification accuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has a higher degree of success in classifying sleep stages compared to the existing methods.

کلیدواژه‌ها [English]

  • Discrete wavelet transform
  • Automatic Sleep Stage Detection
  • Ant colony optimization algorithm
  • RUSBoost

[1]   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]   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]   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]Garrett, Deon, David A. Peterson, Charles W. Anderson, and Michael H. Thaut. "Comparison of linear, nonlinear, and feature selection methods for EEG signal classification." IEEE Transactions on neural systems and rehabilitation engineering 11, no. 2, pp 141-144, 2003

[11]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.

[12]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.

[13]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

[14]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.

[15]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.

[16]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.

[17]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.

[18]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.

[19]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.

[20]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.

[21]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.

[22]Ocak, Hasan. "Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm." Signal processing 88, no. 7 pp. 1858-1867, 2008

[23]Ghosh-Dastidar, Samanwoy, Hojjat Adeli, and Nahid Dadmehr. "Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection." IEEE transactions on biomedical engineering 54, no. 9 pp: 1545-1551, 2007

[24]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.

[25]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.

[26]M. Dorigo, V. Maniezzo, A. Colorni,"the Ant System: Optimization by a Colony of Cooperating Agent", IEEE Transactions on Systems, Man and Cybernetics, Part B 26 (1), pp. 29-41, 1996.

[27]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.

[28]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.

[29]T. Seiffert, J. Khoshgoftaar, A. Van Hulse, "Napolitano, Rusboost: a hybrid ap- proach to alleviating class imbalance", IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. vol. 40 no. 1, pp 185–197, 2010.

[30]S. Sheykhivand, T. Yousefi Rezaii, Z. Mousavi, S. Meshgini; "Automatic Stage Scoring of Single-Channel Sleep EEG using CEEMD of Genetic Algorithm and Neural Network"; Iranian journal of Computational Intelligence in Electrical Engineering, vol. 9, no. 4, pp 15-28, 2018.

[31]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.

[32]K. P. Murphy, "Naive bayes classifiers," University of British Columbia, vol. 18, 2006.

[33]Ahmadlou, Mehran, and Hojjat Adeli. "Enhanced probabilistic neural network with local decision circles: A robust classifier." Integrated Computer-Aided Engineering 17, no. 3, pp: 197-210, 2010

[34] J. Izenman, "Linear discriminant analysis," in Modern multivariate statistical techniques: Springer, 2013, pp. 237-280.

[35]Berthomier et al., "Automatic analysis of single-channel sleep EEG: validation in healthy individuals," Sleep, vol. 30, no. 11, pp. 1587-1595, 2007.

[36]م. آذرنوش، م. اکبرزاده توتونچی، «تشخیص خودکار مراحل خواب با استفاده از جدول جستجوی فازی» مجموعه مقالات یازدهمین کنفرانس سالانه انجمن کامپیوتر، تهران ، ایران، صفحه 356-360، آذر 1384.