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EEG Based Automatic Sleep Staging via Simple 2D-Convolutional Neural Network

Year 2022, Volume: 8 Issue: 3, 491 - 498, 31.12.2022

Abstract

Sleep disorders have high prevalence and cause various health problems. For the diagnostics of these disorders and assessment of the sleep quality, many physiological data are collected using polysomnogram (PSG) method. The most important PSG data is the EEG recorded from the brain during sleep. Analysis of hours of sleep EEG data by experts is an onerous task which requires high attention. Recently, many automatic sleep staging classifiers using EEG are developed in order to prevent human error, and to provide a quick objective analysis. They use machine learning techniques and predict the sleep stage of each EEG epoch. Compared to traditional machine learning, deep learning which requires no hand-crafted feature extraction was able to classify sleep stages better. 1D Convolutional Neural Networks (CNN) are the main methods used in automatic sleep staging recently. In this research a simple 2D-CNN based automatic sleep staging feasibility is investigated. It has been found that a 2D CNN can classify the sleep stages by accuracy of 92.55% and with a Cohen’s kappa of 0.82.

References

  • [1] W.H. Spriggs, Essentials of Polysomnography; Jones & Bartlett Learning: Burlington, MA, USA, 2014.
  • [2] H. Schulz, “Rethinking sleep analysis,” Journal of Clinical Sleep Medicine. vol. 4 no. 2, pp. 99–103, 2008
  • [3] T. Hori, Y. Sugita, E. Koga, S. Shirakawa, K. Inoue, S. Uchida,; H. Kuwahara, M. Kousaka, T. Kobayashi, Y. Tsuji, et al. Proposed supplements and amendments to ‘A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects’, the Rechtscha_en & Kales (1968) standard. Psychiatry Clin. Neurosci., 55, 305–310. 2001.
  • [4] Carley, D.W.; Farabi, S.S. Physiology of sleep. Diabetes Spectr. 29, 5–9. 2016
  • [5] Özen G., Sultanov R., Özen Y., Güneş Z.Y. A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging. Sakarya University Journal of Computer and Information Sciences, 3(2), 149-158. 2020.
  • [6] Šušmákov K. "Human sleep and sleep EEG." Measurement science review 4.2 pp. 59-74, 2004.
  • [7] Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C. and Vaughn, B.V., 2012. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine, 176, p.2012.
  • [8] Mousavi, Z., T. Yousefi Rezaii, S. Sheykhivand, A. Farzamnia, and S. N. Razavi. "Deep convolutional neural network for classification of sleep stages from single-channel EEG signals." Journal of neuroscience methods 324 (2019): 108312.
  • [9] Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of neural engineering. 2019 Apr 9;16(3):031001.
  • [10] Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J, Zhang X. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316. 2016 Apr 25.
  • [11] Phan H, Andreotti F, Cooray N, Chén OY, De Vos M. Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering. 2018 Oct 22;66(5):1285-96.
  • [12] Kemp, B.; Zwinderman, A.; Tuk, B.; Kamphuisen, H.; Oberye, J. Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 2000, 47, 1185–1194. [10]
  • [13] Zhou, D., Hu, G., Zhang, J., Wang, J., Yan, R., Li, F., ... & Cong, F. (2021). SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG. bioRxiv, 2020-09.
  • [14] Xu K, Xia S, Li G. Automatic Classification of Sleep Stages Based on Raw Single-Channel EEG. InChinese Conference on Pattern Recognition and Computer Vision (PRCV) 2020 Oct 16 (pp. 356-368). Springer, Cham.
  • [15] Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint arXiv:1610.01683. 2016 Oct 5.
  • [16] Cai Q, Gao Z, An J, Gao S, Grebogi C. A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals. IEEE Transactions on Circuits and Systems II: Express Briefs. 2020 Aug 5;68(2):777-81.
  • [17] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017 Jun 28;25(11):1998-2008.
  • [18] Fu M, Wang Y, Chen Z, Li J, Xu F, Liu X, Hou F. Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography. Frontiers in Physiology. 2021 Mar 3;12:179.
  • [19] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PloS one. 2019 May 7;14(5):e0216456.
  • [20] Khalili E, Asl BM. Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG. Computer Methods and Programs in Biomedicine. 2021 Jun 1;204:106063.
  • [21] Salamatian A, Khadem A. Automatic sleep stage classification using 1D convolutional neural network. Frontiers in Biomedical Technologies. 2020 Sep 30;7(3):142-50
  • [22] Yildirim O, Baloglu UB, Acharya UR. A deep learning model for automated sleep stages classification using PSG signals. International journal of environmental research and public health. 2019 Jan;16(4):599.

2 Boyutlu Evrişimsel Sinir Ağları ile EEG Tabanlı Otomatik Uyku Evrelerinin Sınıflandırılması

Year 2022, Volume: 8 Issue: 3, 491 - 498, 31.12.2022

Abstract

Uyku bozuklukları toplumda oldukça yaygın görülmekle birlikte çeşitli sağlık sorunlarına neden olmaktadır. Bu bozuklukların teşhis edilmesi ve uyku kalitesinin belirlenmesi için Polisomnogram metodu ile birçok fizyolojik veri toplanılır. En önemli data uyku halinde beyinden kaydedilen EEG verisidir. Saatler süren uykuya ait EEG verilerinin uzmanlar tarafından analiz edilmesi yüksek dikkat isteyen çok zahmetli bir iştir. Son zamanlarda insan hatalarını önlemek ve hızlı nesnel bir analiz gerçekleştirmek amacıyla EEG sinyallerini kullanan otomatik uyku evre sınıflandırıları geliştirilmiştir. Bu sınıflandırıcılar makine öğrenmesi yöntemlerini kullanır ve her bir EEG kesitine dair uyku evresini tahmin eder. Geleneksel makine öğrenmesi yöntemlerine kıyasla elle hiçbir öznitelik çıkarımı gerektirmeyen derin öğrenme uyku evre sınıflandırmasında daha başarılı olabilmiştir. Son zamanlarda, tek boyutlu evrişimsel sinir ağları otomatik uyku evre sınıflandırmasında ana yöntem olmuştur. Bu araştırmada iki boyutlu basit bir evrişimsel sinir ağlarına dayalı otomatik uyku evre sınıflandırılmasının uygulanabilirliği incelenmiştir. iki boyutlu evrişimsel sinir ağlarının %92.5 doğruluk ve 0.82 Cohen Kappa değeri ile sınıflandırmabildiği bulunmuştur.

References

  • [1] W.H. Spriggs, Essentials of Polysomnography; Jones & Bartlett Learning: Burlington, MA, USA, 2014.
  • [2] H. Schulz, “Rethinking sleep analysis,” Journal of Clinical Sleep Medicine. vol. 4 no. 2, pp. 99–103, 2008
  • [3] T. Hori, Y. Sugita, E. Koga, S. Shirakawa, K. Inoue, S. Uchida,; H. Kuwahara, M. Kousaka, T. Kobayashi, Y. Tsuji, et al. Proposed supplements and amendments to ‘A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects’, the Rechtscha_en & Kales (1968) standard. Psychiatry Clin. Neurosci., 55, 305–310. 2001.
  • [4] Carley, D.W.; Farabi, S.S. Physiology of sleep. Diabetes Spectr. 29, 5–9. 2016
  • [5] Özen G., Sultanov R., Özen Y., Güneş Z.Y. A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging. Sakarya University Journal of Computer and Information Sciences, 3(2), 149-158. 2020.
  • [6] Šušmákov K. "Human sleep and sleep EEG." Measurement science review 4.2 pp. 59-74, 2004.
  • [7] Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C. and Vaughn, B.V., 2012. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine, 176, p.2012.
  • [8] Mousavi, Z., T. Yousefi Rezaii, S. Sheykhivand, A. Farzamnia, and S. N. Razavi. "Deep convolutional neural network for classification of sleep stages from single-channel EEG signals." Journal of neuroscience methods 324 (2019): 108312.
  • [9] Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: a review. Journal of neural engineering. 2019 Apr 9;16(3):031001.
  • [10] Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J, Zhang X. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316. 2016 Apr 25.
  • [11] Phan H, Andreotti F, Cooray N, Chén OY, De Vos M. Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering. 2018 Oct 22;66(5):1285-96.
  • [12] Kemp, B.; Zwinderman, A.; Tuk, B.; Kamphuisen, H.; Oberye, J. Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 2000, 47, 1185–1194. [10]
  • [13] Zhou, D., Hu, G., Zhang, J., Wang, J., Yan, R., Li, F., ... & Cong, F. (2021). SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG. bioRxiv, 2020-09.
  • [14] Xu K, Xia S, Li G. Automatic Classification of Sleep Stages Based on Raw Single-Channel EEG. InChinese Conference on Pattern Recognition and Computer Vision (PRCV) 2020 Oct 16 (pp. 356-368). Springer, Cham.
  • [15] Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint arXiv:1610.01683. 2016 Oct 5.
  • [16] Cai Q, Gao Z, An J, Gao S, Grebogi C. A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals. IEEE Transactions on Circuits and Systems II: Express Briefs. 2020 Aug 5;68(2):777-81.
  • [17] Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017 Jun 28;25(11):1998-2008.
  • [18] Fu M, Wang Y, Chen Z, Li J, Xu F, Liu X, Hou F. Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography. Frontiers in Physiology. 2021 Mar 3;12:179.
  • [19] Mousavi S, Afghah F, Acharya UR. SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PloS one. 2019 May 7;14(5):e0216456.
  • [20] Khalili E, Asl BM. Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG. Computer Methods and Programs in Biomedicine. 2021 Jun 1;204:106063.
  • [21] Salamatian A, Khadem A. Automatic sleep stage classification using 1D convolutional neural network. Frontiers in Biomedical Technologies. 2020 Sep 30;7(3):142-50
  • [22] Yildirim O, Baloglu UB, Acharya UR. A deep learning model for automated sleep stages classification using PSG signals. International journal of environmental research and public health. 2019 Jan;16(4):599.
There are 22 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Conference Paper
Authors

İbrahim Kaya 0000-0003-0802-4376

Publication Date December 31, 2022
Submission Date December 27, 2021
Acceptance Date October 27, 2022
Published in Issue Year 2022 Volume: 8 Issue: 3

Cite

IEEE İ. Kaya, “EEG Based Automatic Sleep Staging via Simple 2D-Convolutional Neural Network”, GJES, vol. 8, no. 3, pp. 491–498, 2022.

Gazi Journal of Engineering Sciences (GJES) publishes open access articles under a Creative Commons Attribution 4.0 International License (CC BY). 1366_2000-copia-2.jpg