Abstract
Sleep stages are determined firstly for the evaluation of sleep quality and the diagnosis of sleep diseases. The signals, recorded from sensors connected to various parts of the body, such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are used for this purpose. After the production of affordable wearable EEG devices for individual use, studies have begun to detect sleep stages from a single channel EEG signal. This paper presents an automated system that can perform sleep staging using a single-channel raw EEG signal. A Convolutional Neural Network (CNN) model was trained with the raw EEG signal for sleep stage detection. The use of CNN does not require any feature extraction. The developed CNN model classifies the sleep data sampled at 250 Hz, divided into 30-second segments according to the 5-class sleep staging system. According to the test results, the performance of the proposed system was found to be 93% macro F1 score and 92% accuracy.
Keywords
References
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- A. Sors, S. Bonnet, S. Mirek, L. Vercueil and J. Payen, “A Convolutional Neural Network for Sleep Stage Scoring from Raw Single-Channel EEG,” Biomed. Signal Proc. Control, vol. 42, pp. 107-114, 2018.
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- D. Pedamonti, “Comparison of Non-Linear Activation Functions for Deep Neural Networks on Mnist Classification Task,” Arxiv:1804.02763v1, 2018.
Abstract
Uyku kalitesinin değerlendirilmesi ve uyku hastalıklarının teşhisi için öncelikle uyku evreleri tespit edilmektedir. Bunun için vücudun çeşitli bölgelerine bağlı sensörlerden kaydedilen elektroensefalogram (EEG), elektrokardiyogram (ECG), elektrookülogram (EOG), elektromiyogram (EMG) gibi sinyaller kullanılmaktadır. Bireysel kullanım için üretilen uygun fiyatlı giyilebilir EEG cihazlarının üretilmesi ile tek kanallı EEG sinyalinden uyku evreleri tespiti yapılabilmesi için çalışmalar başlamıştır. Bu makalede tek kanallı ham EEG sinyali kullanarak uyku evreleri tespiti yapabilen otomatik bir sistem sunulmaktadır. Bu amaçla ham EEG sinyalleri ile bir Evrişimsel Sinir Ağı (ESA) modeli eğitilmiştir. ESA kullanımı sayesinde herhangi bir özellik çıkarımı yapılmasına ihtiyaç bulunmamaktadır. Geliştirilen ESA modeli 250 Hz’de örneklenmiş, 30 sn’lik epoklara bölünmüş uyku verisini 5 sınıflı uyku evrelemesi sistemine göre sınıflandırmaktadır. Değerlendirme sonuçlarına göre sistemin başarımı %93 makro F1-skoru ve %92 doğruluk olarak bulunmuştur.
Keywords
References
- R. Sharma, R.B. Pachori, and A. Upadhyay, “Automatic Sleep Stages Classification Based on Iterative Filtering of Electroencephalogram Signals,” Neural Computing and Applications, vol. 28, no. 10, pp. 2959-2978, 2017.
- J. A. Hobson, “Sleep: Biochemical Aspects,” New England Journal of Medicine, vol. 281, no. 26, pp. 1468-1470, 1969.
- C. Iber, “Development of A New Manual for Characterizing Sleep,” Journal of Sleep and Sleep Disorder Research, vol. 27, no. 2, pp. 190-192, 2004.
- B. J. Swihart, B. Caffo and K. Bandeen, “Characterizing Sleep Structure Using the Hypnogram,” Journal of Clinical Sleep Medicine, vol. 4, no.04, pp. 349-355, 2008.
- H. Phan, F. Andreotti, N. Cooray and O. Y. Chen, “Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 5, pp.1285 – 1296, 2019.
- A. Supratak, H. Dong, C. Wu and Y. Guo, “DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 1998-2008, 2017.
- S. J. Redmond and C. Heneghan, “Cardiorespiratory-based Sleep Staging in Subjects with Obstructive Sleep Apnea,” IEEE Transactions on Bio-Medical Engineering, vol. 53, no. 3, pp. 485-496, 2006.
- B. Koley and D. Dey, “An Ensemble System for Automatic Sleep Stage Classification Using Single Channel EEG Signal,” Computers in Biology and Medicine, vol. 42, no.12, pp.1186-1195, 2012.
- E. Alickovic, J. Kevric and A. Subasi, “Performance Evaluation of Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packed Decomposition for Automated Epileptic Seizure Detection and Prediction,” Biomedical Signal Processing and Control, vol. 39, pp. 94-102, 2018.
- N. Stephansen, E. Shane, C. Jessica, R. Jason. and R. David, “Survey of Machine Learning Techniques in Drug Discovery,” Current Drug Metabolism, vol. 20, no. 3, pp.185-193, 2019.
- K. O. Shea and R. Nash, 2015. “An Introduction to Convolutional Neural Networks,” Arxiv:1511.08458v2, 2015.
- M. Sokolovsky, F. Guerrero, S. Paisarnsrisomsuk and S. A. Alvarez, “Deep Learning for Automated Feature Discovery and Classification of Sleep Stages,” Transactions of the Institute of Measurement and Control, vol. 38, pp. 435-451, 2018.
- H. Ghimatgar, K. Kazemi, M. S. Helfroush. and A. Aarabi, “An Automatic Single-Channel EEG-Based Sleep Stage Scoring Method Based on Hidden Markov Model,” Journal of Neuroscience Methods,” vol. 324, pp. 270-285, 2019.
- O. Tsinalis, P. M. Matthews and Y. Guo, “Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders,” Annals of Biomedical Engineering, vol. 44, pp. 1587-1597, 2016.
- J. Zhang, R. Yao, W. Ge and J. Gao, “Orthogonal Convolutional Neural Networks for Automatic Sleep Stage Classification Based on Single-Channel EEG,” Transactions of the Institute of Measurement and Control, vol. 38, pp. 435-451, 2019.
- A. Sors, S. Bonnet, S. Mirek, L. Vercueil and J. Payen, “A Convolutional Neural Network for Sleep Stage Scoring from Raw Single-Channel EEG,” Biomed. Signal Proc. Control, vol. 42, pp. 107-114, 2018.
- physionet, “MIT-BIH Polysomnographic Database Files,” 1999. [Online]. Available: https://physionet.org/content/slpdb/1.0.0/. [Accessed: 19-Oct-2019].
- physionet, “MIT-BIH Polysomnographic Database Information,” 1999. [Online]. Available: https://physionet.org/content/slpdb/1.0.0/slpdb.shtml. [Accessed: 21-Oct-2019].
- D. Pedamonti, “Comparison of Non-Linear Activation Functions for Deep Neural Networks on Mnist Classification Task,” Arxiv:1804.02763v1, 2018.
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence |
| Journal Section | Articles |
| Authors | |
| Publication Date | August 28, 2020 |
| Submission Date | May 4, 2020 |
| Acceptance Date | August 12, 2020 |
| Published in Issue | Year 2020 |
Cited By
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https://doi.org/10.35377/saucis.04.01.879735
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