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An Automated Sleep Stages Classification Using BrainEEG Signal: A Machine Learning Approaches

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Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1361))

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Abstract

Sleep is essential for people’s health and well-being. However, numerous individuals face sleep problems. These problems can lead to several neurological and physical disorder diseases, and therefore, decrease their overall life quality. Artificial intelligence methods for automated sleep stage classification (ASSC) are a fundamental approach to evaluate and treat this public health challenge. The main contribution of this paper is to present the design and development of an ASSC. This study supports the recognition of sleep stages and provides relevant information on the sleep process according to the American Academy of Sleep Medicine manuals. The proposed method includes a two-step execution process. On the one hand, the sleep records are extracted through electroencephalogram signals. Three different health condition subjects of distinct gender and different age groups have been analyzed. On the other hand, different session recordings of sleep processes from two additional nights are considered. The proposed work uses a single channel for two-state sleep stage classification. This study uses a public dataset and incorporates data pre-processing, data extraction, and feature selection. The entire experiment was executed on different medical conditioned subjects using a support vector machine (SVM). The reported results signify that the proposed model achieved the best classification accuracy of 97.73% with the subgroup-II subject using SVM classification models, respectively.

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References

  1. L.A. Panossian, A.Y. Avidan, Review of sleep disorders. Med. Clin. N. Am. 93, 407–425 (2009)

    Google Scholar 

  2. A. Smaldone, J.C. Honig, M.W. Byrne, Sleepless in America: inadequate sleep and relationships to health and well-being of our nation’s children. Pediatrics 119, 29–37 (2007)

    Article  Google Scholar 

  3. A.R. Hassan, M.I. Bhuiyan, Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern. Biomed. Eng. (2016)

    Google Scholar 

  4. K. Aboalayon, H.T. Ocbagabir, Faezipour, Efficient sleep stage classification based on EEG signals, in Systems, Applications and Technology Conference (LISAT) (2014), pp. 1–6

    Google Scholar 

  5. Obayya, F. AbouChadi, Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm, in Radio Science Conference (NRSC) (2014), pp. 265–272

    Google Scholar 

  6. E. Alickovic, A. Subasi, Ensemble SVM method for automatic sleep stage classification. IEEE Trans. Instrum. Meas. (2018)

    Google Scholar 

  7. U.R. Abeyratne, V. Swarnkar, S.I. Rathnayake, C. Hukins, Sleep-stage and event de-pendency of brain asynchrony as manifested through surface EEG, in Proceedings of the 29th IEEE Annual International Conference of the Engineering in Medicine and Biology Society (2007), pp. 709–712

    Google Scholar 

  8. A. Rechtschaffen, A. Kales, A manual of standardized terminology, techniques and scoring systems for sleep stages of human subjects. U.G.P. Office, Public Health Service; Washington, DC, USA, 1968

    Google Scholar 

  9. C. Iber, S. Ancoli-Israel, Chesson, S.F. Quan, The AASM manual for the scoring of sleep and associated events: rules. Terminol. Tech. Specific. American Academy of Sleep Medicine (2007)

    Google Scholar 

  10. M.T. Bianchi, Sleep Devices: wearables and nearables, informational and interventional, consumer and clinical metabolism clinical. Experimental (2017)

    Google Scholar 

  11. D. Cogan, J. Birjandtalab, M. Nourani, J. Harvey, V. Nagaraddi, Multi-biosignal analysis for epileptic seizure monitoring. Int. J. Neural Syst. (2017)

    Google Scholar 

  12. M. Obayya, F. Abou-Chadi, Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm, in Radio Science Conference (NRSC), (2014), pp. 265–272

    Google Scholar 

  13. K. Polat, Güneş, Ş. Yosunkaya, Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Syst. Appl. 37, 7922–7928 (2010)

    Google Scholar 

  14. K. Aboalayon, H.T. Ocbagabir, M. Faezipour, Efficient sleep stage classification based on EEG signals, in Systems, Applications and Technology Conference (LISAT) (2014), pp. 1–6

    Google Scholar 

  15. A.R. Hassan, A. Subasi, A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl.-Based Syst. 128, 115–124 (2017)

    Article  Google Scholar 

  16. M. Diykh, Y. Li, P. Wen, EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 1159–1168 (2016)

    Article  Google Scholar 

  17. K.M. Gunnarsdottir, C.E. Gamaldo, R.M.E. Salas, J.B. Ewen, R.P. Allen, S.V. Sarma, A novel sleep stage scoring system: combining expert-based rules with a decision tree classifier, in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018)

    Google Scholar 

  18. N. Sriraam, T.K. PadmaShri, U. Maheshwari, Recognition of wake-sleep stage 1 multichannel EEG patterns using spectral entropy features for drowsiness detection. Australas. Phys. Eng. Sci. Med. 39, 797–806 (2018)

    Article  Google Scholar 

  19. P. Memar, F. Faradji, A novel multi-class EEG-based sleep stage classification system. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 84–95 (2018)

    Article  Google Scholar 

  20. T.L.T. Da. Silveira, A.J. Kozakevicius, C.R. Rodrigues, Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Med. Biol. Eng. Compu. 55, 343–352 (2016)

    Article  Google Scholar 

  21. B. Wutzl, K. Leibnitz, F. Rattay, M. Kronbichler, M. Murata, Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness. PLOS ONE 14 (2019)

    Google Scholar 

  22. G. Zhu, Y. Li, P.P. Wen, Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE J. Biomed. Health Inform. 18, 1813–2182 (2014)

    Article  Google Scholar 

  23. M.K. Delimayanti, B. Purnama, N.G. Nguyen, M.R. Faisal, K.R. Mahmudah, F. Indriani, K. Satou, Classification of brainwaves for sleep stages by high-dimensional FFT features from EEG signals. Appl. Sci. 10 (2020)

    Google Scholar 

  24. S. Khalighi, T. Sousa, J.M. Santos, U. Nunes, ISRUC-sleep: a comprehensive public dataset for sleep researchers. Comput. Methods Programs Biomed. 124, 180–192 (2016)

    Google Scholar 

  25. S. Eskandari, M.M. Javidi, Online streaming feature selection using rough sets. Int. J. Approx. Reason. 69, 35–57 (2016)

    Google Scholar 

  26. B. Scholkopf, A. Smola, Learning with Kernels. MIT Press (2002)

    Google Scholar 

  27. H.O. İlhan, G. Bilgin, Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals. Int. J. Intell. Syst. Appl. Eng. 5, 174–184 (2017)

    Google Scholar 

  28. T.H. Sanders, M. McCurry, M.A. Clements, Sleep stage classification with cross frequency coupling, in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2014), pp. 4579–4582

    Google Scholar 

  29. V. Bajaj, R. Pachori, Automatic classification of sleep stages based on the time-frequency image of EEG signals. Comput. Methods Programs Biomed. 112, 320–328 (2013)

    Article  Google Scholar 

  30. Y.-L. Hsu, Y.-T. Yang, J.-S. Wang, C.-Y. Hsu, Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 104, 105–114 (2013)

    Article  Google Scholar 

  31. I. Zibrandtsen, P. Kidmose, M. Otto, J. Ibsen, T.W. Kjaer, Case comparison of sleep features from ear-EEG and scalp-EEG. Sleep Sci. 9, 69–72 (2016)

    Article  Google Scholar 

  32. R.B. Berry, R. Brooks, C.E. Gamaldo, S.M. Hardsim, R.M. Lloyd, C.L. Marcus, B.V. Vaughn, The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Am. Acad. Sleep Med. (2014)

    Google Scholar 

  33. S.J. Sim, C.C. Wright, The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys. Therapy 85, 257–268 (2005)

    Google Scholar 

  34. S.-F. Liang, C.-E. Kuo, Y.H. Kuo, Y.-S. Cheng, A rule-based automatic sleep staging method. J. Neurosci. Methods 205, 169–176 (2012)

    Google Scholar 

  35. S.-F. Liang, C.-E. Kuo, Y.-H. Hu, Y.-H. Pan, Y.-H. Wang, Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. 61 (2012)

    Google Scholar 

  36. J. Kim, A comparative study on classification methods of sleep stages by using EEG. J. Korea Multimed. Soc. 17, 113–123 (2014)

    Article  Google Scholar 

  37. M. Peker, A new approach for automatic sleep scoring: combining Taguchi based complex-valued neural network and complex wavelet transform. Comput. Methods Programs Biomed. 129, 203–216 (2016)

    Article  Google Scholar 

  38. A. Subasi, M.K. Kiymik, M. Akin, O. Erogul, Automatic recognition of vigilance state by using a wavelet-based artificial neural network. Neural Comput. Appl. 14, 45–55 (2005)

    Article  Google Scholar 

  39. M.E. Tagluk, N. Sezgin, M. Akin, Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J. Med. Syst. 34, 717–725 (2010)

    Article  Google Scholar 

  40. A.R. Hassan, M.I.H. Bhuiyan, An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting. Neurocomputing 219, 76–87 (2017)

    Article  Google Scholar 

  41. A.R. Hassan, M.I.H. Bhuiyan, Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput. Methods Programs Biomed. 140, 201–210 (2017)

    Article  Google Scholar 

  42. M. Diykh, Y. Li, Complex networks approach for EEG signal sleep stages classification. Expert Syst. Appl. 63, 241–248 (2016)

    Article  Google Scholar 

  43. S.M. Mohammadi, S. Kouchaki, M. Ghavami, S. Sanei, Improving time–frequency domain sleep EEG classification via singular spectrum analysis. J. Neurosci. Methods 273, 96–106 (2016)

    Google Scholar 

  44. B. Şen, M. Peker, A. Çavuşoğlu, F.V. Çelebi, A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 38 (2014)

    Google Scholar 

  45. N. Burioka et al., Approximate entropy in the electroencephalogram during wake and sleep. Clin. EEG Neurosci. 36, 21–24 (2005)

    Google Scholar 

  46. M. Obayya, F.E.Z. Abou-Chadi, Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm, in 2014 31st National Radio Science Conference (NRSC) (2014), pp. 265–272

    Google Scholar 

  47. L. Fraiwan, K. Lweesy, N. Khasawneh, M. Fraiwan, H. Wenz, H. Dickhaus, Classification of sleep stages using multi-wavelet time frequency entropy and LDA. Methods Inf. Med. 49, 230–237 (2010)

    Article  Google Scholar 

  48. L.J. Herrera et al., Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int. J. Neural Syst. 23 (2013)

    Google Scholar 

  49. M. Radha, G. Garcia-Molina, M. Poel, G. Tononi, Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal, in 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2014), pp. 1876–1880

    Google Scholar 

  50. H.G. Jo, J.Y. Park, C.K. Lee, S.K. An, S.K. Yoo, Genetic fuzzy classifier for sleep stage identification. Comput. Biol. Med. 40, 629–634 (2010)

    Article  Google Scholar 

  51. L.J. Herrera, A.M. Mora, C.M. Fernandes, Symbolic representation of the EEG for sleep stage classification, in 11th International Conference on Intelligent Systems Design and Applications (2011), pp. 253–258

    Google Scholar 

  52. S.A. Vanbelle, New interpretation of the weighted Kappa coefficients. Psychometrika 81, 399–410 (2016)

    Article  MathSciNet  Google Scholar 

  53. S. Khalighi, S.T. Sousa, D. OliveiraPires, G.U. Nunes, Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2011)

    Google Scholar 

  54. H. Simões, G. Pires, U. Nunes, V. Silva, Feature extraction and selection for automatic sleep staging using EEG, in Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, vol. 3 (2010), pp. 128–133

    Google Scholar 

  55. T. Sousa, A. Cruz, S. Khalighi, G. Pires, U. Nunes, A two-step automatic sleep stage classification method with dubious range detection. Comput. Biol. Med. 59, 42–53 (2015)

    Article  Google Scholar 

  56. S. Khalighi, T. Sousa, G. Pires, U. Nunes, Automatic sleep staging: a computer assisted approach for optimal combination of features and polysomnographic channels. Expert Syst. Appl. 40, 7046–7059 (2013)

    Article  Google Scholar 

  57. K.D. Tzimourta, A.K. Tsilimbaris, A.T. Tzioukalia, M.G. Tzallas, L.G. Tsipouras, EEG-based automatic sleep stage classification. Biomed. J. Sci. Tech. Res. 7 (2018)

    Google Scholar 

  58. S. Najdi, A. Gharbali, J.M. Fonseca, Feature transformation based on stacked sparse autoencoders for sleep stage classification, in Technological Innovation for Smart Systems (2017), pp. 191–200

    Google Scholar 

  59. H. Kalbkhani, P. Ghasemzadeh, M.G. Shayesteh, Sleep stages classification from EEG signal based on Stockwell transform. IET Signal Process. (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Pondicherry Engineering College, Puducherry, India

    Santosh Kumar Satapathy & D. Loganathan

  2. Godavari Institute of Engineering & Technology, Rajahmundry, Andhra Pradesh, India

    M. V. Sangameswar

Authors
  1. Santosh Kumar Satapathy
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  2. M. V. Sangameswar
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  3. D. Loganathan
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Corresponding author

Correspondence to Santosh Kumar Satapathy .

Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, Anna University, Chennai, India

    S. Chandramohan

  2. Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Bala Venkatesh

  3. Department of Electrical and Electronics Engineering, Government College of Engineering Keonjh, Jamunalia, India

    Subhransu Sekhar Dash

  4. Electronics and Communication Sciences, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Department of Electrical and Electronics Engineering, Anna University, Chennai, Guindy, India

    C. Sharmeela

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Satapathy, S.K., Sangameswar, M.V., Loganathan, D. (2022). An Automated Sleep Stages Classification Using BrainEEG Signal: A Machine Learning Approaches. In: Chandramohan, S., Venkatesh, B., Sekhar Dash, S., Das, S., Sharmeela, C. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1361. Springer, Singapore. https://doi.org/10.1007/978-981-16-2674-6_24

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