Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG
AbstractSleep is a normal state in humans and the subconscious level of brain activity increases during sleep. The brain plays a prominent role during sleep, so a variety of mental and brain-related diseases can be identified through sleep analysis. A complete sleep period according to the two world standards R&K and AASM consists of seven and five steps, respectively. To diagnose diseases through sleep, it is necessary to identify different stages of sleep because the disorder at each stage indicates a certain disease. On the other hand, efficient and useful features should be selected to increase the accuracy of sleep stage classification. In this paper, at first, different statistical, entropy, and chaotic features are extracted from sleep data. Afterwards, by introducing and using the Laplacian score selector, the best feature set is selected. At the end, some conventional classification algorithms such as SVM, ANN and KNN are used to classify different sleep stages. Simulation results confirms the superiority of the proposed method based on the classification results. With the proposed algorithm, 2, 3, 4, 5 and 6 stages of sleep were classified by SVM and decision tree with 98.0%, 98.0%, 97.3%, 96.6%, and 95.0% accuracy, which are more superior to previous method’s results.
 A. Sors, S. Bonnet, S. Mirek, L. Vercueil, J.-F. J. B. S. P. Payen, and Control, “A convolutional neural network for sleep stage scoring from raw single-channel eeg,” Biomedical Signal Processing and Control, Vol. 42, pp. 107-114, 2018.
 E. Kakar, L. J. Corel, R. C. Tasker, R. de Goederen, E. B. Wolvius, I. M. Mathijssen, and K. F. J. S. m. Joosten, “Electrocardiographic variables in children with syndromic craniosynostosis and primary snoring to mild obstructive sleep apnea: significance of identifying respiratory arrhythmia during sleep,” Sleep medicine, Vol. 45, pp. 1-6, 2018.
 E. Toffol, M. Lahti-Pulkkinen, J. Lahti, J. Lipsanen, K. Heinonen, A.-K. Pesonen, E. Hämäläinen, E. Kajantie, H. Laivuori, and P. M. J. S. m. Villa, “Maternal depressive symptoms during and after pregnancy are associated with poorer sleep quantity and quality and sleep disorders in 3.5-year-old offspring,” 2018.
 S.-Y. Tsai, W.-T. Lee, S.-F. Jeng, C.-C. Lee, and W.-C. J. J. o. P. H. C. Weng, “Sleep and Behavior Problems in Children With Epilepsy,” Journal of Pediatric Health, vol. 33, no. 2, pp. 138-145, 2019.
 A. J. B. i. s. Rechtschaffen, “A manual for standardized terminology, techniques and scoring system for sleep stages in human subjects,” Brain information service, 1968.
 C. Iber, and C. Iber, :The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications", American Academy of Sleep Medicine Westchester, IL, 2007.
 S. Seifpour, H. Niknazar, M. Mikaeili, and A. M. Nasrabadi, “A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal,” Expert Systems with Applications, vol. 104, pp. 277-293, 2018.
 Q. Al-Tashi, S. J. A. Kadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection,” IEEE Access, Vol. 7, pp. 39496-39508, 2019.
 P. Memar, and F. Faradji, “A novel multi-class EEG-based sleep stage classification system,” IEEE TRANSACTIONS ON NEURAL SYSTEMS, Vol. 26, No. 1, pp. 84-95, 2018.
 M. M. Rahman, M. I. H. Bhuiyan, and A. R. Hassan, “Sleep stage classification using single-channel EOG,” Computers in biology medicine, Vol. 102, pp. 211-220, 2018.
 N. El Aboudi, and L. Benhlima, "Review on wrapper feature selection approaches." pp. 1-5.
 H. Rao, X. Shi, A. K. Rodrigue, J. Feng, Y. Xia, M. Elhoseny, X. Yuan, and L. Gu, “Feature selection based on artificial bee colony and gradient boosting decision tree,” Applied Soft Computing, Vol. 74, pp. 634-642, 2019.
 D. Cho, and B. Lee, "Optimized automatic sleep stage classification using the normalized mutual information feature selection (NMIFS) method." pp. 3094-3097.
 S. Akhter, U. R. Abeyratne, V. Swarnkar, and C. J. J. o. C. S. M. Hukins, “Snore sound analysis can detect the presence of obstructive sleep apnea specific to NREM or REM sleep,” Journal of Clinical Sleep, Vol. 14, No. 06, pp. 991-1003, 2018.
 X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection." pp. 507-514.
 A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. J. C. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” AHA journals, Vol. 101, No. 23, pp. e215-e220, 2000.
 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.
 M. Ronzhina, O. Janoušek, J. Kolářová, M. Nováková, P. Honzík, and I. J. S. m. r. Provazník, “Sleep scoring using artificial neural networks,” Vol. 16, No. 3, pp. 251-263, 2012.
 B. Hjorth, “EEG analysis based on time domain properties,” Electroencephalography and clinical neurophysiology, Vol. 29, No. 3, pp. 306-310, 1970.
 S. Hartmann, and M. Baumert, “Automatic a-phase detection of cyclic alternating patterns in sleep using dynamic temporal information,” IEEE Transactions on Neural Systems Rehabilitation Engineering, Vol. 27, No. 9, pp. 1695-1703, 2019.
 S. Bayatfar, S. Seifpour, M. A. Oskoei, and A. Khadem, "An Automated System for Diagnosis of Sleep Apnea Syndrome Using Single-Channel EEG Signal." pp. 1829-1833.
 T. Inouye, K. Shinosaki, H. Sakamoto, S. Toi, S. Ukai, A. Iyama, Y. Katsuda, and M. Hirano, “Quantification of EEG irregularity by use of the entropy of the power spectrum,” Electroencephalography clinical neurophysiology, Vol. 79, No. 3, pp. 204-210, 1991.
 J. Lin, “Divergence measures based on the Shannon entropy,” IEEE Transactions on Information theory, Vol. 37, No. 1, pp. 145-151, 1991.
 P. Perrot, A to Z of Thermodynamics: Oxford University Press on Demand, 1998.
 J. L. Lebowitz, “Boltzmann's entropy and time's arrow,” Physics today, Vol. 46, pp. 32-32, 1993.
 T. W. Deacon, “Shannon–Boltzmann–Darwin: Redefining information (Part I),” Cognitive Semiotics, Vol. 1, No. fall2007, pp. 123-148, 2007.
 M. Belkin, and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering." pp. 585-591.
 K. Benabdeslem, and M. Hindawi, "Constrained laplacian score for semi-supervised feature selection." pp. 204-218.
 J. Zhao, K. Lu, and X. He, “Locality sensitive semi-supervised feature selection,” Neurocomputing, Vol. 71, No. 10-12, pp. 1842-1849, 2008.
 W. Malina, “On an extended Fisher criterion for feature selection,” IEEE Transactions on pattern analysis machine intelligence, no. 5, pp. 611-614, 1981.
 Q. Gu, Z. Li, and J. Han, “Generalized fisher score for feature selection,” arXiv preprint arXiv, 2012.
 Z. Zhao, L. Wang, and H. Liu, "Efficient spectral feature selection with minimum redundancy."
 R. J. Little, and D. B. Rubin, “Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches,” Annual review of public health, Vol. 21, No. 1, pp. 121-145, 2000.
 K. J. Berry, and P. W. Mielke Jr, “A generalization of Cohen's kappa agreement measure to interval measurement and multiple raters,” Educational Psychological Measurement, Vol. 48, No. 4, pp. 921-933, 1988.
 J. Cohen, “A coefficient of agreement for nominal scales,” Educational psychological measurement, Vol. 20, No. 1, pp. 37-46, 1960.
 M. Gaiduk, T. Penzel, J. A. Ortega, and R. Seepold, “Automatic sleep stages classification using respiratory, heart rate and movement signals,” Physiological measurement, Vol. 39, No. 12, pp. 124008, 2018.
 S. Taran, P. C. Sharma, and V. Bajaj, “Automatic sleep stages classification using optimize flexible analytic wavelet transform,” Knowledge-Based Systems, Vol. 192, pp. 105367, 2020.
 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 programs in biomedicine, Vol. 140, pp. 201-210, 2017.
 A. R. Hassan, and A. Subasi, “A decision support system for automated identification of sleep stages from single-channel EEG signals,” Knowledge-Based Systems, Vol. 128, pp. 115-124, 2017.
 D. Jiang, Y.-n. Lu, M. Yu, and W. Yuanyuan, “Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement,” Expert Systems with Applications, Vol. 121, pp. 188-203, 2019.
 A. R. Hassan, M. I. H. J. B. Bhuiyan, and B. Engineering, “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.
 B. K. Kanoje, A. S. J. I. J. o. A. R. i. C. E. Shingare, and Technology, “Automatic sleep stage detection of an EEG signal using an ensemble method,” International Journal of Advanced Research in Computer Engineering & Technology, Vol. 3, No. 8, 2014.