Uyku evrelerinin sınıflandırılmasında EEG ve EOG sinyallerinin karşılaştırılması

Yaşamın en önemli parçası olan uykunun değeri, uykusuzluğun neden olduğu sağlık sorunlarının ortaya çıkmasıyla birlikte artmaktadır. Bu sorunu çözmek için uyku evrelerinde ortaya çıkan farklı sinyal kalıplarını yorumlamak son derece önemlidir. Bu amaca ulaşmak için uyku evrelerinin otomatik olarak puanlanmasını sağlayan sistemler oluşturulur. Uyku puanlamasında uyuyan kişinin elektrofizyolojik sinyalleri dikkate alınarak uyku hakkında değerli bilgiler elde edilir. Çalışmada uyku alanında çalışan araştırmacılara açık erişim olarak sunulan ISRUC-Sleep veri seti kullanılmıştır. Çalışmanın temel amacı, uyku evrelerinin sınıflandırılmasında elektroensefalografi (EEG) ve elektrookülografi (EOG) biyosinyallerinin etkisini araştırmaktır. Analiz, ISRUC platformuna ait üç farklı grubu tanımlayan veri setinin üçüncü grubu dikkate alınarak gerçekleştirilmiştir. Veri setindeki alt grup_3'ün 10 katılımcısı dikkate alınmıştır. Etkili öznitelikler çıkarılarak ve farklı sınıflandırma yöntemleri uygulanarak aşamaların sınıflandırılmasında EEG veya EOG sinyallerinden hangisinin daha iyi olduğu araştırılmıştır. Kullanılan sınıflandırma yöntemlerinin performans değerlendirmesi açısından önceki çalışmamızda sunulan yeni Roza metriği uygulanmıştır. Welch öznitelik çıkarma yöntemi ve toplu ağaç sınıflandırma tekniği sayesinde uyku evrelerinin sınıflandırılmasında EEG sinyallerinin EOG'dan daha başarılı olduğu kanıtlanmıştır. Bu uyku evreleri EEG sinyallerini kullanarak %77.7 başarı oranıyla sınıflandırılmıştır.

Comparison of EEG and EOG signals in classification of sleep stages

The value of sleep, which is the most significant part of life, increases with the emergence of health problems caused by insomnia. To solve this problem, it is extremely important to interpret the different signal patterns that occur during sleep stages. In order to achieve this goal, systems are created that provide automatic scoring of sleep stages. In sleep scoring, valuable information about sleep is obtained by considering the electrophysiological signals of the sleeper. The ISRUCSleep dataset, which was presented as open access to researchers working in the field of sleep, was used in the study. The main goal of the study is to investigate the effect of electroencephalography (EEG) and electrooculography (EOG) biosignals in the classification of sleep stages. The analysis was carried out by considering the third group of the data set, which defines three different groups belonging to the ISRUC platform. The 10 participants of subgrup_3 in the dataset were considered. By extracting effective features and applying different classification methods, it was investigated which one of the EEG or EOG signals was better in the classification of stages. In terms of performance evaluation of the classification methods used, the new Roza metric presented in our previous study was applied. It has been proven that EEG signals are more successful than EOG in the classification of sleep stages, thanks to the Welch feature extraction method and the ensemble of bagged tree classification technique. These sleep stages were classified by using EEG signals with a success rate of 77.7%.

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  • [1] Tan DEB, Tung RS, Leong WY, Than JCM. “Sleep disorder detection and ıdentification”. Procedia Engineering, 41, 289-295, 2012.
  • [2] Aboalayon KAI, Faezipour M, Almuhammadi WS, Moslehpour S. “Sleep stage classification using eeg signal analysis: a comprehensive survey and new ınvestigation”. Entropy 2016, 18, 18(9), 1-31, 2016.
  • [3] Khalighi S, Sousa T, Pires G, Nunes U. “Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels”. Expert Systems with Applications, 40(17), 7046-7059, 2013.
  • [4] Ohayon MM. “Epidemiology of insomnia: what we know and what we still need to learn”. Sleep Medicine Reviews, 6(2), 97-111, 2002.
  • [5] Lee YH, Chen YS, Chen LF. “Automated sleep staging using single EEG channel for REM sleep deprivation”. Proc 2009 9th IEEE International Conference Bioinforma Bioeng BIBE 2009, Taichung, Taiwan, 22-24 June 2009.
  • [6] Leistedt S, Dumont M, Lanquart JP, Jurysta F, Linkowski P. “Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis”. Clinical Neurophysiology, 118(4), 940-950, 2007.
  • [7] Khalighi S, Sousa T, Santos JM, Nunes U. “ISRUC-Sleep: A comprehensive public dataset for sleep researchers”. Computer Methods and Programs in Biomedicine, 124, 180-192, 2016.
  • [8] Nonoue S, Mashita M, Haraki S, Mikami A, Adachi H, Yatani H, Yoshida A, Taniike M, Kato T. “Inter-scorer reliability of sleep assessment using EEG and EOG recording system in comparison to polysomnography”. Sleep and Biological Rhythms, 15(1), 39-48, 2017.
  • [9] Fiorillo L, Puiatti A, Papandrea M, Ratti PL, Favaro P, Roth C, Bargiotas P, Bassetti CL, Faraci FD. “Automated sleep scoring: A review of the latest approaches”. Sleep Medicine Reviews, 48, 1-12, 2019.
  • [10] Chesson AL, Ferber RA, Fry JM, Grigg-Damberger M, Hartse KM, Hurwitz TD, Johnson S, Kader G A, Littner M, Rosen G, Sangal R B, Schmidt-Nowara W, Sher A.“The ındications for polysomnography and related procedures”. Sleep, 20(6), 423-487, 1997.
  • [11] Voinescu BI, Wislowska M, Schabus M. “Assessment of SOMNOwatch plus EEG for sleep monitoring in healthy individuals”. Physiology & Behavior, 132, 73-78, 2014.
  • [12] Edward A, Wolpert MD. “A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects”. Archives of General Psychiatry, 20(2), 246-247, 1969.
  • [13] Penzel T, Conradt R. “Computer based sleep recording and analysis”. Sleep Medicine Reviews, 4(2), 131-148, 2000.
  • [14] Moser D, Anderer P, Gruber G, Parapatics S, Loretz EE, Boeck M, Kloesch G, Heller E, Schmidt A, Danker-Hopfe H, Saletu B, Zeitlhofer J, Dorffner G. “Sleep classification according to AASM and Rechtschaffen & Kales: Effects on sleep scoring parameters”. Sleep, 32(2), 139-149, 2009.
  • [15] Himanen SL, Hasan J. “Limitations of Rechtschaffen and Kales”. Sleep Medicine Reviews, 4(2), 149-167, 2000.
  • [16] Berry RB, Budhiraja R, Gottlieb DJ, Gozal D, Iber C, Kapur VK, Marcus CL, Mehra, R, Parthasarathy S, Quan SF, Redline S, Strohl KP, Ward SL, Tangredi MM. “Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. deliberations of the sleep apnea definitions task force of the american academy of sleep medicine”. Journal of Clinical Sleep Medicine, 8(5), 597-619, 2012.
  • [17] Tagluk ME, Sezgin N, Akin M. “Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG”. Journal of Medical Systems, 34(4), 717-725, 2010.
  • [18] Horner RL. “Pathophysiology of obstructive sleep apnea”. Journal of Cardiopulmonary Rehabilitation and Prevention, 28(5), 289-298, 2008.
  • [19] Hazarika N, Chen JZ, Tsoi AC, Sergejew A. “Classification of EEG signals using the wavelet transform”. Signal Processing, 59(1), 61-72, 1997.
  • [20] Uyku Evrelerini Skorlama Kriterleri. “Tüm Uyku Tıbbı ve Araştırmaları Derneği”. Available from: http://tutder.org.tr/uyku-evrelerini-skorlama-kriterleri/ 12.05.2020.
  • [21] Hassan AR, Bhuiyan MIH. “A decision support system for automatic sleep staging from EEG signals using tunable Qfactor wavelet transform and spectral features”. Journal of Neuroscience Methods, 271, 107-118, 2016.
  • [22] Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H. “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, 108(1), 10-19, 2012.
  • [23] Guzel S, Kaya T, Guler H. “LabVIEW-based analysis of EEG signals in determination of sleep stages”. 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16-19 May 2015.
  • [24] Rosenberg RS, Hout S Van. “The American academy of sleep medicine ınter-scorer reliability program: sleep stage scoring”. Journal of Clinical Sleep Medicine, 9(1), 81-87, 2013.
  • [25] Muto V, Berthomier C, Schmidt C, Vandewalle G, Jaspar M, Devillers J, Chellappa S, Meyer C, Phillips C, Berthomier P, Prado J, Benoit O, Brandewinder M, Mattout J, Maquet P. “0315 ınter- and ıntra-expert variability ın sleep scoring: comparison between visual and automatic analysis”. Sleep, 41(suppl_1), A121-A121, 2018.
  • [26] Younes M, Raneri J, Hanly P. “Staging sleep in polysomnograms: analysis of ınter-scorer variability”. Journal of Clinical Sleep Medicine, 12(6), 885-894, 2016.
  • [27] Sharma M, Goyal D, Achuth PVP, Acharya UR. “An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank”. Computers in Biology and Medicine, 98, 58-75, 2018.
  • [28] Ebrahimi F, Setarehdan SK, Nazeran H. “Automatic sleep staging by simultaneous analysis of ECG and respiratory signals in long epochs”. Biomedical Signal Processing and Control, 18, 69-79, 2015.
  • [29] da Silveira TLT, Kozakevicius AJ, Rodrigues CR. “Singlechannel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain”. Medical & Biological Engineering & Computing, 55(2), 343-352, 2017.
  • [30] Pillay K, Dereymaeker A, Jansen K, Naulaers G, Van Huffel S, De Vos M. “Automated EEG sleep staging in the term-age baby using a generative modelling approach”. Journal of Neural Engineering, 15(3), 1-12, 2018.
  • [31] Holzmann CA, Pérez CA, Held CM, San Martín M, Pizarro F, Pérez JP, Garrido M, Peirano P. “Expert-system classification of sleep/waking states in infants”. Medical & Biological Engineering & Computing, 37(4), 466-476, 1999.
  • [32] Oropesa E, Cycon H, Jobert M. “Sleep stage classification using wavelet transform and neural network”. Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China, 05-07 January 2012.
  • [33] Agarwal R, Gotman J. “Computer-assisted sleep staging”. IEEE Transactions on Biomedical Engineering, 48(12), 1412-1423, 2001.
  • [34] Estrada E, Nazeran H, Nava P, Behbehani K, Burk J, Lucas E. “EEG feature extraction for classification of sleep stages”. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, USA, 01-05 September 2004.
  • [35] Becq G, Charbonnier S, Chapotot F, Buguet A, Bourdon L, Baconnier P. Comparison Between Five Classifiers for Automatic Scoring of Human Sleep Recordings. Editors: Halgamuge K, Wang S. Studies in Computational Intelligence, 113-127, Berlin, Heidelberg, Springer Press, 2005.
  • [36] Sinha RK. “Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states”. Journal of Medical Systems, 32(4), 291-299, 2008.
  • [37] Chapotot F, Becq G. “Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules”. Signal Processing for Monitoring and Diagnosis: Medical Applications, 24(5), 409-423, 2009.
  • [38] Jo HG, Park JY, Lee CK, An SK, Yoo SK. “Genetic fuzzy classifier for sleep stage identification”. Computers in Biology and Medicine, 40(7), 629-634, 2010.
  • [39] Özşen S. “Classification of sleep stages using classdependent sequential feature selection and artificial neural network”. Neural Computing and Applications, 23(5), 1239-1250, 2012.
  • [40] Ronzhina M, Janoušek O, Kolářová J, Nováková M, Honzík P, Provazník I. “Sleep scoring using artificial neural networks”. Sleep Medicine Reviews, 16(3), 251-263, 2012.
  • [41] Hsu YL, Yang YT, Wang JS, Hsu CY. “Automatic sleep stage recurrent neural classifier using energy features of EEG signals”. Neurocomputing, 104, 105-114, 2013.
  • [42] Şen B, Peker M, Çavuşoğlu A, Çelebi F V. “A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms”. Journal of Medical Systems, 38(3), 1-21, 2014.
  • [43] Güneş S, Polat K, Yosunkaya Ş. “Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting”. Expert Systems with Applications, 37(12), 7922-7928, 2010.
  • [44] Acharya UR, Chua ECP, Chua KC, Min LC, Tamura T. “Analysis and automatic identification of sleep stages using higher order spectra”. International Journal of Neural Systems, 20(6), 509-521, 2010.
  • [45] Sharma R, Pachori RB, Upadhyay A. “Automatic sleep stages classification based on iterative filtering of electroencephalogram signals”. Neural Computing and Applications, 28(10), 2959-2978, 2017.
  • [46] Shen H, Xu M, Guez A, Li A, Ran F. “An accurate sleep stages classification method based on state space model”. IEEE Access, 7, 125268-125279, 2019.
  • [47] Santaji S, Desai V. “Analysis of EEG Signal to Classify Sleep Stages Using Machine Learning”. Sleep and Vigilance, 4(2), 145-152, 2020.
  • [48] Melek M, Manshouri N, Kayikcioglu. “An automatic EEGbased sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems”. Cognitive Neurodynamics, 15(3), 405-423, 2021.
  • [49] Quan SF, Howard BV, Iber C, Kiley JP, Nieto FJ, O’Connor GT, Rapoport DM, Redline S, Robbins J, IJonathan M, Patricia W. “The sleep heart health study: design, rationale, and methods”. Sleep, 20(12), 1077-1085, 1997.
  • [50] O’Reilly C, Gosselin N, Carrier J, Nielsen T. “Montreal archive of sleep studies: an open-access resource for instrument benchmarking and exploratory research”. Journal of Sleep Research, 23(6), 628-635, 2014.
  • [51] Tsanas A, Clifford GD. “Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing”. Frontiers in Human Neuroscience, 9, 1-15, 2015.
  • [52] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus J, Moody G B, Peng CK, Stanley HE. “PhysioBank, physiotoolkit, and PhysioNet: components of a new research resource for complex physiologic signals”. Circulation, 101(23), 215-220, 2000.
  • [53] Adnane M, Jiang Z, Yan Z. “Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram”. Expert Systems with Applications, 39(1), 1401-1413, 2012.
  • [54] Varun B, Ram BP. “Automatic classification of sleep stages based on the time-frequency image of EEG signals”. Computer Methods and Programs in Biomedicine, 112(3), 320-328, 2013.
  • [55] Yaghouby F, Sunderam S. “Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables”. Computers in Biology and Medicine, 59, 54-63, 2015.
  • [56] Klem GH, Lüders HO, Jasper HH, Elger C. “The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology”. Electroencephalography and Clinical Neurophysiology Supplement Journal, 52, 3-6, 1999.
  • [57] Malmivuo J, Plonsey R. Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields, New York, USA, Oxford University Press, 2012.
  • [58] Melek M, Melek N. “Roza: a new and comprehensive metric for evaluating classification systems”. Computer Methods in Biomechanics and Biomedical Engineering, 25(9), 1015-1027, 2021.
  • [59] Sousa T, Cruz A, Khalighi S, Pires G, Nunes U. “A two-step automatic sleep stage classification method with dubious range detection”. Computers in Biology and Medicine, 59, 42-53, 2015.
  • [60] Manshouri N, Melek M, Kayıkcıoglu T. “Detection of 2D and 3D video transitions based on EEG power”. Computer Journal, 65(2), 396-409, 2022.
  • [61] Prihanditya H, Prihanditya N. “The implementation of Zscore normalization and boosting techniques to ıncrease accuracy of C4.5 algorithm in diagnosing chronic kidney disease”. Journal of Soft Computing Exploration, 1(1), 63-69, 2020.
  • [62] Harris FJ. “On the use of windows for harmonic analysis with the discrete fourier transform”. Proceedings of the IEEE, 66(1), 51-83, 1978.
  • [63] Yanwu X, Xianbin C, Hong Q. “An efficient tree classifier ensemble-based approach for pedestrian detection”. IEEE Transactions on Systems, Man, and Cybernetics, 41(1), 107-117, 2011.
  • [64] Gudivada VN, Irfan MT, Fathi E, Rao DL. “Cognitive analytics: going beyond big data analytics and machine learning”. Handbook of Statistics, 35, 169-205, 2016.
  • [65] Brijain M, Patel R, Kushik M. “A Survey on decision tree algorithm for classification”. International Journal of Engineering Development and Research, 2, 1-5, 2014.
  • [66] Widasari ER, Tanno K, Tamura H. “A new ınvestigation of automatic sleep stage detection using decision-tree-based support vector machine and spectral features extraction of ECG signal”. IEEJ Transactions on Electronics, Information and Systems, 139(7), 820-827, 2019.
  • [67] Brodley CE, Friedl MA. “Decision tree classification of land cover from remotely sensed data”. Remote Sensing of Environment, 61(3), 399-409, 1997.
  • [68] Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D. “Top 10 algorithms in data mining”. Knowledge and Information Systems, 14(1), 1-37, 2007.
  • [69] Mathworks. “Fit Binary Decision Tree for Multiclass Classification-MATLABFitctree”. https://www.mathworks.com/help/stats/fitctree.html# bujbrib-6 (22.12.2020).
  • [70] Fix E, Hodges JL. “Discriminatory analysis. nonparametric discrimination: consistency properties”. International Statistical Review, 57(3), 238, 1989.
  • [71] Kaya T, Türk M, Kaya D. “Examining the effect of dimension reduction on eeg signals by knearest neighbors algorithm”. El-Cezeri Journal of Science and Engineering, 5(2), 591-595, 2018.
  • [72] Grenander U, Duda RO, Hart PE. Pattern Classification and Scene Analysis. 1st ed. New York, USA, A Wiley-Interscience Publication, 1973.
  • [73] Timuş OH, Bolat ED. “k-NN-based classification of sleep apnea types using ECG”. Turkish Journal of Electrical Engineering and Computer Sciences, 25(4), 3008-3023, 2017.
  • [74] Rechichi I, Zibetti M, Borzì L, Olmo G, Lopiano L. “Single‐ channel EEG classification of sleep stages based on REM microstructure”. Healthcare Technology Letters, 8(3), 58-65, 2021.
  • [75] Virkkala J, Hasan J, Värri A, Himanen SL, Müller K. “Automatic sleep stage classification using two-channel electro-oculography”. Journal of Neuroscience Methods, 166(1), 109-115, 2007.
  • [76] Brignol A, Al-ani T, Drouot X. “Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths”. Computer Methods and Programs in Biomedicine, 109(3), 227-238, 2013.
  • [77] Fraiwan L, Lweesy K, Khasawneh N, Fraiwan M, Wenz H, Dickhaus H. “Classification of sleep stages using multiwavelet time frequency entropy and LDA”. Methods of Information in Medicine, 49(3), 230-237, 2010.
  • [78] Kemp B, Zwinderman AH, Tuk B, HAC K, JJL O. “Analysis of a sleep-dependent neuronal feedback loop: The slowwave microcontinuity of the EEG”. IEEE Transactions on Biomedical Engineering, 47(9), 1185-1194, 2000.
  • [79] Terzano MG, Parrino L, Sherieri A, Chervin R, Chokroverty S, Guilleminault C, Hirshkowitz M, Mahowald M, Moldofsky H, Rosa A, Thomas R, Walters A. “Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep”. Sleep Medicine, 2(6), 537-553, 2001.
  • [80] Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR. “A review of automated sleep stage scoring based on physiological signals for the new millennia”. Computer Methods and Programs in Biomedicine, 176, 81-91, 2019.
  • [81] Shi J, Liu X, Li Y, Zhang Q, Li Y, Ying S. “Multi-Channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning”. Journal of Neuroscience Methods, 254, 94-101, 2015.
  • [82] Rahman MM, Bhuiyan MIH, Hassan AR. “Sleep stage classification using single-channel EOG”. Computers in Biology and Medicine, 102, 211-220, 2018.
  • [83] Yan R, Zhang C, Spruyt K, Wei L, Wang Z, Tian L, Li X, Ristaniemi T, Zhang J, Cong F. “Multi-modality of polysomnography signals’ fusion for automatic sleep scoring”. Biomedical Signal Processing Control, 49, 14-23, 2019.
  • [84] Ni H, Zhao T, Zhou X, Wang Z, Chen L, Yang J. “Analyzing sleep stages in home environment based on ballistocardiography”. Lecture Notes in Computer Science, 9085, 56-68, 2015.
  • [85] Estrada E, Nazeran H, Barragan J, Burk JR, Lucas EA, Behbehani K. “EOG and EMG: Two important switches in automatic sleep stage classification”. 2006 International Conference of the IEEE Engineering in Medicine and Biology Society , New York , USA, 30 August- 03 September 2006.
  • [86] Ghimatgar H, Kazemi K, Helfroush MS, Aarabi A. “An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model”. Journal of Neuroscience Methods, 324, 1-46, 2019.
  • [87] Shen H, Ran F, Xu M, Guez A, Li A, Guo A. “An automatic sleep stage classification algorithm using ımproved model based essence features”. Sensors (Basel), 20(17), 1-21, 2020.
Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi-Cover
  • ISSN: 1300-7009
  • Yayın Aralığı: 7
  • Başlangıç: 1995
  • Yayıncı: PAMUKKALE ÜNİVERSİTESİ
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