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Uyku evrelerinin sınıflandırılmasında EEG ve EOG sinyallerinin karşılaştırılması

Yıl 2023, Cilt: 29 Sayı: 6, 607 - 616, 30.11.2023

Öz

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.

Kaynakça

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Comparison of EEG and EOG signals in classification of sleep stages

Yıl 2023, Cilt: 29 Sayı: 6, 607 - 616, 30.11.2023

Öz

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%.

Kaynakça

  • [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.
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  • [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.
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Toplam 87 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği (Diğer)
Bölüm Makale
Yazarlar

Negin Melek

Yayımlanma Tarihi 30 Kasım 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 29 Sayı: 6

Kaynak Göster

APA Melek, N. (2023). Comparison of EEG and EOG signals in classification of sleep stages. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 29(6), 607-616.
AMA Melek N. Comparison of EEG and EOG signals in classification of sleep stages. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Kasım 2023;29(6):607-616.
Chicago Melek, Negin. “Comparison of EEG and EOG Signals in Classification of Sleep Stages”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29, sy. 6 (Kasım 2023): 607-16.
EndNote Melek N (01 Kasım 2023) Comparison of EEG and EOG signals in classification of sleep stages. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29 6 607–616.
IEEE N. Melek, “Comparison of EEG and EOG signals in classification of sleep stages”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 29, sy. 6, ss. 607–616, 2023.
ISNAD Melek, Negin. “Comparison of EEG and EOG Signals in Classification of Sleep Stages”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29/6 (Kasım 2023), 607-616.
JAMA Melek N. Comparison of EEG and EOG signals in classification of sleep stages. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29:607–616.
MLA Melek, Negin. “Comparison of EEG and EOG Signals in Classification of Sleep Stages”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 29, sy. 6, 2023, ss. 607-16.
Vancouver Melek N. Comparison of EEG and EOG signals in classification of sleep stages. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29(6):607-16.





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