Research Article
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Classification of Sleep Stages from Polysomnography Signals with Deep Learning and Machine Learning Methods

Year 2023, Volume: 13 Issue: 2, 583 - 600, 15.06.2023

Sinan Altun

https://doi.org/10.31466/kfbd.1246482
Cited By: 1

Abstract

Sleep is an important time of activity for the daily renewal of our physical and mental health, and it occupies a third of our lives. Sleep disorders can exacerbate or cause symptoms of psychiatric disorders. The first of these may be sleep apnea. Another cause is restless legs syndrome. Depression, anxiety, pain, and some physical problems can also cause insomnia. Sleep apnea can be caused by a nervous system problem or airway obstruction. Studying sleep stages is crucial in diagnosing sleep-related disorders. Sleep stages are also determined by a professional by being with the person during sleep. Considering the average sleep stage diagnosis time of 8 hours, this is quite a long time for a professional. In addition, the definition of sleep stages requires serious expertise and knowledge. The computerized diagnosis system, which automatically diagnoses and treats the diseases described in the literature, has started to be implemented based on theoretical research. This study aims to use deep learning and machine learning techniques to automatically generate sleep stages, which are important parameters in the diagnosis of sleep disorders that directly affect human health. In this study, the random forest algorithm performed the most successful classification (accuracy = 0.974, sensitivity = 0.932, specificity = 0.983). This advanced classification success demonstrates the feasibility of creating a computer-aided diagnostic system that can automatically identify sleep stages, which is an important factor in the diagnosis/treatment of sleep-related disorders.

Keywords

Polysomnography Deep learning Machine learning Sleep disorders Sleep stages

References

  • Abdulla, S., Diykh, M., Siuly, S., Ali, M. (2023). An intelligent model involving multi-channels Spectrum Patterns based features for automatic sleep stage classification. International Journal of Medical Informatics, 171, 105001. https://doi.org/10.1016/j.ijmedinf.2023.105001
  • Altun, S., Alkan, A. (2022). MR spektroskopi kullanılarak beyin tümörü tespitinde lstm tabanlı derin öğrenme uygulaması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi. https://doi.org/10.17341/gazimmfd.1069632
  • Altun, S., Alkan, A., Altun, H. (2021). The investigation of wisc-r profiles in children with border intelligence and intellectual disability with machine learning algorithms. Pamukkale University Journal of Engineering Sciences, 27(5), 589–596. https://doi.org/10.5505/pajes.2020.53077
  • Arslan, H., Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CPG island features and KNN classifier. Engineering Science and Technology, an International Journal, 24(4), 839–847. https://doi.org/10.1016/j.jestch.2020.12.026
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
  • Coelli, S., Medina Villalon, S., Bonini, F., Velmurugan, J., López-Madrona, V. J., Carron, R., Bartolomei, F., Badier, J.-M., Bénar, C.-G. (2023). Comparison of beamformer and ICA for Dynamic Connectivity Analysis: A simultaneous Meg-Seeg Study. NeuroImage, 265, 119806. https://doi.org/10.1016/j.neuroimage.2022.119806
  • Colten, H.R., Altevogt B.M., 2006. Sleep Disorder and Sleep Deprivation: An Unmet Public Health Problem. National Academies Press, Washington DC. S(33-39).
  • Daş B., Türkoğlu İ., (2014, Kasım). DNA dizilimlerinin sınıflandırılmasında karar ağacı algoritmalarının karşılaştırılması. Eleco 2014 Elektrik – Elektronik – Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu(s. 381-383). Bursa.
  • Demirci M., (2019). Destek vektör makineleri ve m5 karar ağacı yöntemleri kullanılarak yağış-akış ilişkisinin tahmini. DÜMF Mühendislik Dergisi, 10(3),1113-1124. https://doi.org/10.24012/dumf.525658
  • Driver, H. S., Mclean, H., Kumar, D. V., Farr, N., Day, A. G., Fitzpatrick, M. F. (2005). The influence of the menstrual cycle on upper airway resistance and breathing during sleep. Sleep, 28(4), 449–456. https://doi.org/10.1093/sleep/28.4.449
  • Fogel, S. M., Smith, C. T. (2011). The function of the sleep spindle: A physiological index of Intelligence and a mechanism for sleep-dependent memory consolidation. Neurosci Biobehav Rev, 35(5), 1154–1165. https://doi.org/10.1016/j.neubiorev.2010.12.003
  • Ghassemi, M., Moody, B., Lehman, L.-wei, Song, C., Li, Q., Sun, H., Westover, B., Clifford, G. (2018). You Snooze, you win: The PHYSIONET/computing in cardiology challenge 2018. 2018 Computing in Cardiology Conference (CinC). https://doi.org/10.22489/cinc.2018.049
  • Guo, H., Di, Y., An, X., Wang, Z.,; Ming, D. (2022). A novel approach to automatic sleep stage classification using forehead electrophysiological signals. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e12136
  • Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Johnson, J. M., Curtis, F., Durrant, S. J. (2022). Characterising the relationship between sleep stages and associated spectral power in diabetes. Sleep Epidemiology, 2, 100048. https://doi.org/10.1016/j.sleepe.2022.100048
  • Koyanagi, I., Tezuka, T., Yu, J., Srinivasan, S., Naoi, T., Yasugaki, S., Nakai, A., Taniguchi, S., Hayashi, Y., Nakano, Y.,; Sakaguchi, M. (2023). Fully automatic REM sleep stage-specific intervention systems using single EEG in Mice. Neuroscience Research, 186, 51–58. https://doi.org/10.1016/j.neures.2022.10.001
  • Lu, C., Sun, C., Xu, Y., Chen, C., Li, Q. (2022). Polysomnography findings in preschool children with obstructive sleep apnea are affected by growth and developmental level. International Journal of Pediatric Otorhinolaryngology, 162, 111310. https://doi.org/10.1016/j.ijporl.2022.111310
  • Martín-Montero, A., Armañac-Julián, P., Gil, E., Kheirandish-Gozal, L., Álvarez, D., Lázaro, J., Bailón, R., Gozal, D., Laguna, P., Hornero, R., Gutiérrez-Tobal, G. C. (2023). Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability. Computers in Biology and Medicine, 154, 106549. https://doi.org/10.1016/j.compbiomed.2023.106549
  • Ngiam, J., Chen, Z., Bhaskar, S. A., Koh, P. W., Ng, A. Y. (2011). Sparse filtering. Neural Information Processing Systems, 24, 1125–1133. https://papers.nips.cc/paper/4334-sparse-filtering.pdf
  • Nocedal, J., Wright, S. J. (2006). Numerical optimization. Verlag New York:Springer.
  • Powers W., Ailab A. (2008). Evaluation: from precision, recall and f-measure to roc informed ness, markedness and correlation, J. Mach. Learn. Technolgy, 2, 2229-3981.
  • Silber, M. H., Ancoli-Israel, S., Bonnet, M. H., Chokroverty, S., Grigg-Damberger, M. M., Hirshkowitz, M., Kapen, S., Keenan, S. A., Kryger, M. H., Penzel, T., Pressman, M. R., Iber, C. (2007). The visual scoring of sleep in adults. Journal of Clinical Sleep Medicine, 03(02), 121–131. https://doi.org/10.5664/jcsm.26814
  • Stone, K. C., Taylor, D. J., McCrae, C. S., Kalsekar, A., Lichstein, K. L. (2008). Nonrestorative sleep. Sleep Medicine Reviews, 12(4), 275–288. https://doi.org/10.1016/j.smrv.2007.12.002
  • Subudhi A., Dash B. M., Sabut S. (2020). Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics and Biomedical Engineering, 40(1), 277-289. https://doi.org/10.1016/j.bbe.2019.04.004 .
  • Sünnetci, K. M., Alkan, A. (2022). Lung cancer detection by using probabilistic majority voting and Optimization Techniques. International Journal of Imaging Systems and Technology, 32(6), 2049–2065. https://doi.org/10.1002/ima.22769
  • Sunnetci, K. M., Alkan, A. (2023). Biphasic majority voting-based comparative covid-19 diagnosis using chest X-ray images. Expert Systems with Applications, 216, 119430. https://doi.org/10.1016/j.eswa.2022.119430
  • Šušmáková, K., 2004. Human sleep and sleep EEG, Measurement in Biomedicine, 4(2), 69-74.
  • URL1:https://medium.com/@gulcanogundur/do%C4%9Fruluk-accuracy-kesinlik-precision-duyarl%C4%B1l%C4%B1k-recall-ya-da-f1-score-300c925feb38, (Erişim Tarihi: 24 Aralık 2022).
  • URL2: https://ch.mathworks.com/help/stats/cvpartition.html (Erişim Tarihi:28.12.2022).
  • URL3: https://physionet.org/content/challenge-2018/1.0.0/ (Erişim Tarihi:8.1.2023).
  • URL4: https://static1.squarespace.com/static/5459a5d0e4b09a5cc2e5497a/t/54f8d3dbe4b03ea829c7ef53 (Erişim Tarihi:8.1.2023).
  • Yarğı V., Postalcıoğlu S., (2021). EEG işareti kullanılarak bağımlılığa yatkınlığın makine öğrenmesi teknikleri ile analizi. El-Cezerî Journal of Science and Engineering, 8(1), 142-154, DOI :10.31202/ecjse.787726. https://dergipark.org.tr/en/download/article-file/1263790 .

Derin Öğrenme ve Makine Öğrenmesi Yöntemleriyle Polisomnografi Sinyallerinden Uyku Evrelerinin Sınıflandırılması

Year 2023, Volume: 13 Issue: 2, 583 - 600, 15.06.2023

Sinan Altun

https://doi.org/10.31466/kfbd.1246482
Cited By: 1

Abstract

Uyku, fiziksel ve zihinsel sağlığımızın günlük olarak yenilenmesi için önemli bir aktivite zamanıdır ve yaşamımızın üçte birini kaplar. Uyku bozuklukları, psikiyatrik bozuklukları şiddetlendirebilir veya semptomlarına neden olabilir. Bunlardan ilki uyku apnesi olabilir. Diğer bir neden ise huzursuz bacak sendromudur. Depresyon, anksiyete, ağrı ve bazı fiziksel problemler de uykusuzluğa neden olabilir. Uyku apnesi, sinir sistemi probleminden veya soluk yolu tıkanıklığından kaynaklanabilir. Uyku evrelerini incelemek, uyku ile ilgili bozuklukların teşhisinde çok önemlidir. Uyku evreleri de uyku sırasında kişinin yanında olunarak bir profesyonel tarafından belirlenir. Ortalama 8 saatlik uyku evre teşhis süresi düşünüldüğünde, bu bir profesyonel için oldukça uzun bir süredir. Ayrıca uyku evrelerinin tanımlanması ciddi bir uzmanlık ve bilgi birikimi gerektirmektedir. Literatürde tanımlanan hastalıkların teşhis ve tedavi sürecini otomatik olarak yapan bilgisayarlı teşhis sistemi teorik araştırmalara dayalı olarak uygulanmaya başlandı. Bu çalışma, insan sağlığını doğrudan etkileyen uyku bozukluklarının teşhisinde önemli parametreler olan uyku evrelerini otomatik olarak oluşturmak için derin öğrenme ve makine öğrenmesi tekniklerini kullanmayı amaçlamaktadır. Bu çalışmada, rastgele orman algoritması en başarılı sınıflandırmayı (doğruluk = 0,974, duyarlılık = 0,932, özgüllük = 0,983) gerçekleştirmiştir. Bu gelişmiş sınıflama başarısı, uykuyla ilişkili bozuklukların teşhisinde/tedavisinde önemli bir faktör olan uyku evrelerini otomatik olarak belirleyebilen bilgisayar destekli bir teşhis sistemi oluşturmanın uygulanabilirliğini göstermektedir.

Keywords

Polisomnografi Derin Öğrenme Makine Öğrenmesi Uyku Bozuklukları Uyku Evreleri

References

  • Abdulla, S., Diykh, M., Siuly, S., Ali, M. (2023). An intelligent model involving multi-channels Spectrum Patterns based features for automatic sleep stage classification. International Journal of Medical Informatics, 171, 105001. https://doi.org/10.1016/j.ijmedinf.2023.105001
  • Altun, S., Alkan, A. (2022). MR spektroskopi kullanılarak beyin tümörü tespitinde lstm tabanlı derin öğrenme uygulaması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi. https://doi.org/10.17341/gazimmfd.1069632
  • Altun, S., Alkan, A., Altun, H. (2021). The investigation of wisc-r profiles in children with border intelligence and intellectual disability with machine learning algorithms. Pamukkale University Journal of Engineering Sciences, 27(5), 589–596. https://doi.org/10.5505/pajes.2020.53077
  • Arslan, H., Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CPG island features and KNN classifier. Engineering Science and Technology, an International Journal, 24(4), 839–847. https://doi.org/10.1016/j.jestch.2020.12.026
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/a:1010933404324
  • Coelli, S., Medina Villalon, S., Bonini, F., Velmurugan, J., López-Madrona, V. J., Carron, R., Bartolomei, F., Badier, J.-M., Bénar, C.-G. (2023). Comparison of beamformer and ICA for Dynamic Connectivity Analysis: A simultaneous Meg-Seeg Study. NeuroImage, 265, 119806. https://doi.org/10.1016/j.neuroimage.2022.119806
  • Colten, H.R., Altevogt B.M., 2006. Sleep Disorder and Sleep Deprivation: An Unmet Public Health Problem. National Academies Press, Washington DC. S(33-39).
  • Daş B., Türkoğlu İ., (2014, Kasım). DNA dizilimlerinin sınıflandırılmasında karar ağacı algoritmalarının karşılaştırılması. Eleco 2014 Elektrik – Elektronik – Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu(s. 381-383). Bursa.
  • Demirci M., (2019). Destek vektör makineleri ve m5 karar ağacı yöntemleri kullanılarak yağış-akış ilişkisinin tahmini. DÜMF Mühendislik Dergisi, 10(3),1113-1124. https://doi.org/10.24012/dumf.525658
  • Driver, H. S., Mclean, H., Kumar, D. V., Farr, N., Day, A. G., Fitzpatrick, M. F. (2005). The influence of the menstrual cycle on upper airway resistance and breathing during sleep. Sleep, 28(4), 449–456. https://doi.org/10.1093/sleep/28.4.449
  • Fogel, S. M., Smith, C. T. (2011). The function of the sleep spindle: A physiological index of Intelligence and a mechanism for sleep-dependent memory consolidation. Neurosci Biobehav Rev, 35(5), 1154–1165. https://doi.org/10.1016/j.neubiorev.2010.12.003
  • Ghassemi, M., Moody, B., Lehman, L.-wei, Song, C., Li, Q., Sun, H., Westover, B., Clifford, G. (2018). You Snooze, you win: The PHYSIONET/computing in cardiology challenge 2018. 2018 Computing in Cardiology Conference (CinC). https://doi.org/10.22489/cinc.2018.049
  • Guo, H., Di, Y., An, X., Wang, Z.,; Ming, D. (2022). A novel approach to automatic sleep stage classification using forehead electrophysiological signals. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e12136
  • Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Johnson, J. M., Curtis, F., Durrant, S. J. (2022). Characterising the relationship between sleep stages and associated spectral power in diabetes. Sleep Epidemiology, 2, 100048. https://doi.org/10.1016/j.sleepe.2022.100048
  • Koyanagi, I., Tezuka, T., Yu, J., Srinivasan, S., Naoi, T., Yasugaki, S., Nakai, A., Taniguchi, S., Hayashi, Y., Nakano, Y.,; Sakaguchi, M. (2023). Fully automatic REM sleep stage-specific intervention systems using single EEG in Mice. Neuroscience Research, 186, 51–58. https://doi.org/10.1016/j.neures.2022.10.001
  • Lu, C., Sun, C., Xu, Y., Chen, C., Li, Q. (2022). Polysomnography findings in preschool children with obstructive sleep apnea are affected by growth and developmental level. International Journal of Pediatric Otorhinolaryngology, 162, 111310. https://doi.org/10.1016/j.ijporl.2022.111310
  • Martín-Montero, A., Armañac-Julián, P., Gil, E., Kheirandish-Gozal, L., Álvarez, D., Lázaro, J., Bailón, R., Gozal, D., Laguna, P., Hornero, R., Gutiérrez-Tobal, G. C. (2023). Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability. Computers in Biology and Medicine, 154, 106549. https://doi.org/10.1016/j.compbiomed.2023.106549
  • Ngiam, J., Chen, Z., Bhaskar, S. A., Koh, P. W., Ng, A. Y. (2011). Sparse filtering. Neural Information Processing Systems, 24, 1125–1133. https://papers.nips.cc/paper/4334-sparse-filtering.pdf
  • Nocedal, J., Wright, S. J. (2006). Numerical optimization. Verlag New York:Springer.
  • Powers W., Ailab A. (2008). Evaluation: from precision, recall and f-measure to roc informed ness, markedness and correlation, J. Mach. Learn. Technolgy, 2, 2229-3981.
  • Silber, M. H., Ancoli-Israel, S., Bonnet, M. H., Chokroverty, S., Grigg-Damberger, M. M., Hirshkowitz, M., Kapen, S., Keenan, S. A., Kryger, M. H., Penzel, T., Pressman, M. R., Iber, C. (2007). The visual scoring of sleep in adults. Journal of Clinical Sleep Medicine, 03(02), 121–131. https://doi.org/10.5664/jcsm.26814
  • Stone, K. C., Taylor, D. J., McCrae, C. S., Kalsekar, A., Lichstein, K. L. (2008). Nonrestorative sleep. Sleep Medicine Reviews, 12(4), 275–288. https://doi.org/10.1016/j.smrv.2007.12.002
  • Subudhi A., Dash B. M., Sabut S. (2020). Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics and Biomedical Engineering, 40(1), 277-289. https://doi.org/10.1016/j.bbe.2019.04.004 .
  • Sünnetci, K. M., Alkan, A. (2022). Lung cancer detection by using probabilistic majority voting and Optimization Techniques. International Journal of Imaging Systems and Technology, 32(6), 2049–2065. https://doi.org/10.1002/ima.22769
  • Sunnetci, K. M., Alkan, A. (2023). Biphasic majority voting-based comparative covid-19 diagnosis using chest X-ray images. Expert Systems with Applications, 216, 119430. https://doi.org/10.1016/j.eswa.2022.119430
  • Šušmáková, K., 2004. Human sleep and sleep EEG, Measurement in Biomedicine, 4(2), 69-74.
  • URL1:https://medium.com/@gulcanogundur/do%C4%9Fruluk-accuracy-kesinlik-precision-duyarl%C4%B1l%C4%B1k-recall-ya-da-f1-score-300c925feb38, (Erişim Tarihi: 24 Aralık 2022).
  • URL2: https://ch.mathworks.com/help/stats/cvpartition.html (Erişim Tarihi:28.12.2022).
  • URL3: https://physionet.org/content/challenge-2018/1.0.0/ (Erişim Tarihi:8.1.2023).
  • URL4: https://static1.squarespace.com/static/5459a5d0e4b09a5cc2e5497a/t/54f8d3dbe4b03ea829c7ef53 (Erişim Tarihi:8.1.2023).
  • Yarğı V., Postalcıoğlu S., (2021). EEG işareti kullanılarak bağımlılığa yatkınlığın makine öğrenmesi teknikleri ile analizi. El-Cezerî Journal of Science and Engineering, 8(1), 142-154, DOI :10.31202/ecjse.787726. https://dergipark.org.tr/en/download/article-file/1263790 .
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Details

Primary Language Turkish
Subjects Biomedical Engineering
Journal Section Articles
Authors

Sinan Altun 0000-0002-2356-0460

Early Pub Date June 15, 2023
Publication Date June 15, 2023
Published in Issue Year 2023 Volume: 13 Issue: 2

Cite

APA Altun, S. (2023). Derin Öğrenme ve Makine Öğrenmesi Yöntemleriyle Polisomnografi Sinyallerinden Uyku Evrelerinin Sınıflandırılması. Karadeniz Fen Bilimleri Dergisi, 13(2), 583-600. https://doi.org/10.31466/kfbd.1246482

Cited By

A new hybrid feature reduction method by using MCMSTClustering algorithm with various feature projection methods: a case study on sleep disorder diagnosis
Signal, Image and Video Processing
https://doi.org/10.1007/s11760-024-03097-1
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