Skip to main content

Advertisement

SpringerLink
Log in

Multimodal Multiclass Machine Learning Model for Automated Sleep Staging Based on Time Series Data

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Quality sleep is one of the integral parts of the human body, which gives proper rest to the body. It directly promotes and supports maintaining good health, both physically and mentally. With the changes of social rhythm and increased pressure in professional sectors, it causes not to maintain proper sleep daily, which ultimately creates various sleep-related disorders. The first most important step for analyzing any type of sleep disorder is the proper classification of sleep states. The entire research work is conducted through four main steps: signal analysis, feature extraction, feature screening, and classification. This sleep study's main aim is to improve sleep staging by adding multiple channels of physiological signals such as electroencephalogram, electromyogram, and electrooculogram in an automated method. This proposed research work carried four different individual experiments conducted with the input of single-channel EEG, EMG, and EOG signals, finally experiment designed with combinations of all the three channels. This work's main focus is to analyze sleep staging accuracy with individual and combinations of signal recordings. Besides, this research work extracted 30 features, both linear and non-linear, which provide essential information with changes in subjects' sleep behavior. Further, we used a ReliefF feature selection algorithm to select the relevant features highly correlated with sleep stages, which helps analyze the sleep behavior characteristics. This study applied an AdaBoost algorithm with a base classifier as a random forest for five sleep states classification. We considered recordings of five sleep-disordered subjects and five healthy controlled subjects during experiment work the subject's data extracted from the ISRUC-Sleep dataset. The proposed methodology performances are evaluated using ISRUC-Sleep subgroup-I and subgroup-III recordings. The results of the model provide the highest classification accuracy of 98.40%, 98.49%, 98.31%, and 98.52% with EEG, EMG, EOG, and EEG + EMG + EOG respectively with ISRUC-Sleep subgroup-I data, similarly for ISRUC-Sleep subgroup-III data, the reported accuracy reached 97.96%, 98.67%, 98.40%, and 98.46%. The proposed machine learning model is ready to assist clinicians during sleep staging and diagnosis of different types of sleep disorders and can be managed with massive polysomnography records.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Nayak DR, Dash R, Majhi B. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing. 2015;177:188–97.

    Article  Google Scholar 

  2. Hassan AR, Hassan Bhuiyan MI. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern Biomed Eng. 2016;36:248–55.

    Article  Google Scholar 

  3. Alickovic E, Subasi A. Ensemble SVM method for automatic sleep stage classification. IEEE Trans Instrum Meas. 2018;67(6):1258–65.

    Article  Google Scholar 

  4. Satapathy SK, Loganathan D, Sharathkumar S, Narayanan P. Automated sleep staging analysis using sleep EEG signal: a machine learning based model. In: 2021 International conference on advance computing and innovative technologies in engineering (ICACITE), 2021. p. 87–96

  5. Rechtschaffen A, Kales A. A manual of standardized terminology techniques and scoring system for sleep stages of human subjects. Washington, DC: US Government Printing Office, Public Health Service; 1968

    Google Scholar 

  6. Berry RB, Gamaldo CE, Harding SM, Brooks R, Lloyd RM, Vaughn BV, Marcus CL. AASM scoring manual version 2.2 updates: new chapters for scoring infant sleep staging and home sleep apnea testing. J Clin Sleep Med. 2015;11(11):1253–4.

    Article  Google Scholar 

  7. Rosenberg RS, Van Hout S. The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. J Clin Sleep Med. 2013;9(1):81–7.

    Article  Google Scholar 

  8. Satapathy SK, Loganathan D. Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal. Soft Comput. 2021;25:15445–62.

    Article  Google Scholar 

  9. Satapathy SK, Loganathan D, Narayanan P, Sharathkumar S. Convolutional neural network for classification of multiple sleep stages from dual-channel EEG signals. In: 2020 IEEE 4th conference on information & communication technology (CICT), 2020. p. 1–16

  10. Satapathy S, Loganathan D, Kondaveeti HK, et al. Performance analysis of machine learning algorithms on automated sleep staging feature sets. CAAI Trans Intell Technol. 2021;6(2):155–74.

    Article  Google Scholar 

  11. Satapathy SK, Loganathan D. Machine learning approaches with heterogeneous ensemble learning stacking model for automated sleep staging. Int J Comput Digit Syst Univ Bahrain J. 2022;10(1):725–42.

    Article  Google Scholar 

  12. Satapathy SK, Kondaveeti HK. Automated sleep stage analysis and classification based on different age specified subjects from a single-channel of EEG signal. In: 2021 IEEE Madras section conference (MASCON), 2021, p. 1–7

  13. Cogan D, Birjandtalab J, Nourani M, Harvey J, Nagaraddi V. Multi-biosignal analysis for epileptic seizure monitoring. Int J Neural Syst. 2017;27(01):1650031.

    Article  Google Scholar 

  14. Kasabov N, Capecci E. Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Inf Sci. 2015;294:565–75.

    Article  MathSciNet  Google Scholar 

  15. Sinha RK. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. J Med Syst. 2008;32:291–9.

    Article  Google Scholar 

  16. Flexer A, Gruber G, Dorffne G. A reliable Probabilistic sleep stager based on a single EEG signal. Artif Intell Med. 2005;33(3):199–207.

    Article  Google Scholar 

  17. Chapotot F, Becq G. Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules. Int J Adapt Control Signal Process. 2010;24:409–23.

    MathSciNet  MATH  Google Scholar 

  18. Jo HG, Park JY, Lee CK, An SK, Yoo SK. Genetic fuzzy classifier for sleep stage identification. Comput Biol Med. 2010;40(7):629–34.

    Article  Google Scholar 

  19. Subasi A, Kiymik M, Akin M, Erogul O. Automatic recognition of vigilance state by using wavelet-based artificial neural network. Neural Comput Appl. 2005;14(1):45–55.

    Article  Google Scholar 

  20. Zoubek L, Charbonnier S, Lesecq S, Buguet A, Chapotot F. Feature selection for sleep/wake stages classification using data driven methods. Biomed Signal Process Control. 2007;2:171–9.

    Article  Google Scholar 

  21. Gunes S, Polat K, Yonsunkaya S. Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Syst Appl. 2010;37(12):7922–8.

    Article  Google Scholar 

  22. Diykh M, Li Y, Wen P. EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Trans Neural Syst Rehabil Eng. 2016;24(11):1159–68.

    Article  Google Scholar 

  23. Hassan AR, Haque MA. Identification of sleep apnea from single-lead electrocardiogram. In: 2016 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) and 15th international symposium on distributed computing and applications for business Engineering (DCABES), 2016.

  24. Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl Based Syst. 2017;128:115–24.

    Article  Google Scholar 

  25. Hsu YL, Yang YT, Wang JS, Hsu CY. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing. 2013;104:105–14.

    Article  Google Scholar 

  26. Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control. 2016;24:1–10.

    Article  Google Scholar 

  27. Tsinalis O, Matthews PM, Guo Y. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann Biomed Eng. 2016;44(5):1587–97.

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. da Silveira TLT, Kozakevicius AJ, Rodrigues CR. Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Med Biol Eng Comput. 2017;55:343–52.

    Article  Google Scholar 

  30. Gunnarsdottir KM, Gamaldo CE, Salas RME, Ewen JB, Allen RP, Sarma, SV. A novel sleep stage scoring system: combining expert-based rules with a decision tree classifier. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2018.

  31. Dhok S, Pimpalkhute V, Chandurkar A, Bhurane AA, Sharma M, Acharya UR. Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville distribution based features. Comput Biol Med. 2020;119:103691.

    Article  Google Scholar 

  32. Khalighi S, Sousa T, Santos JM, Nunes U. ISRUC-sleep: a comprehensive public dataset for sleep researchers. Comput Methods Programs Biomed. 2016;2016(124):180–92.

    Article  Google Scholar 

  33. Robnik-Šikonja M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn. 2003;53:23–69.

    Article  MATH  Google Scholar 

  34. Kulkarni S, Kelkar V. Classification of multispectral satellite images using ensemble techniques of bagging, boosting and adaboost. In: 2014 International conference on circuits, systems, communication and information technology applications, CSCITA, 2014.

  35. Satapathy SK, Bhoi AK, Loganathan D, Khandelwal B, Barsocchi P. Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal. Biomed Signal Process Control. 2021;69:102898.

    Article  Google Scholar 

  36. Schapire RE. A brief introduction to boosting. Int Jt Conf Artif Intell. 1999;99:1401–6.

    Google Scholar 

  37. Bajaj V, Pachori RB. Automatic classification of sleep stages based on the time-frequency image of EEG signals. Comput Methods Programs Biomed. 2013;112(3):320–8.

    Article  Google Scholar 

  38. Yildiz A, Akin M, Poyraz M, Kirbas G. Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst Appl. 2009;36(4):7390–9.

    Article  Google Scholar 

  39. Sanders TH, McCurry M, Clements MA. Sleep stage classification with cross frequency coupling. In: Proceedings 36th annual international conference IEEE engineering in medicine and biology (EMBC), 2014. p. 4579–4582.

  40. Powers D. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. J. Mach Learn Technol. 2011;2(1):37–63.

    MathSciNet  Google Scholar 

  41. Yuan Y, Jia K, Ma F, et al. A hybrid self-attentiondeep learning framework for multivariate sleep stage classification. BMC Bioinform. 2019;20:586. https://doi.org/10.1186/s12859-019-3075-z.

    Article  Google Scholar 

  42. Langkvist M, Loutfi A. A deep learning approach with an attention mechanism for automatic sleep stage classification. 2018

  43. Huang W, Guo B, Shen Y, Tang X, Zhang T, Li D, Jiang Z. Sleep staging algorithm based on multichannel data adding and multifeature screening. Comput Methods Programs Biomed. 2019;187:105253.

    Article  Google Scholar 

  44. Yildirim O, Baloglu U, Acharya U. A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health. 2019;16(4):599.

    Article  Google Scholar 

  45. Nakamura T, Adjei T, Alqurashi Y, Looney D, Morrell MJ, Mandic DP: Complexity science for sleep stage classification from EEG. In: Proceedings of the international joint conference on neural networks, Anchorage, AK, USA, 2017.

  46. da Silveira TLT, Kozakevicius AJ, Rodrigues CR. Single-channel EEG sleeps stage classification based on a streamlined set of statistical features in wavelet domain. Med Biol Eng Comput. 2017;55:343–52.

    Article  Google Scholar 

  47. Hassan AR, Bhuiyan MIH. Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Programs Biomed. 2017;140:201–10.

    Article  Google Scholar 

  48. Stochholm A, Mikkelsen K, Kidmose P. Automatic sleep stage classification using ear-EEG. Conf Proc IEEE Eng Med Biol Soc. 2016;2016:4751–4.

    Google Scholar 

  49. Rahman MM, Bhuiyan MIH, Hassan AR. Sleep stage classification using single-channel EOG. Comput Biol Med. 2018;102:211–20.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Santosh Kumar Satapathy

  2. Puducherry Technological University, Puducherry, India

    D. Loganathan

Authors
  1. Santosh Kumar Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Loganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santosh Kumar Satapathy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Machine Vision and Augmented Intelligence” guest edited by Manish Kumar Bajpai, Ranjeet Kumar, Koushlendra Kumar Singh and George Giakos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Satapathy, S.K., Loganathan, D. Multimodal Multiclass Machine Learning Model for Automated Sleep Staging Based on Time Series Data. SN COMPUT. SCI. 3, 276 (2022). https://doi.org/10.1007/s42979-022-01156-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01156-3

Keywords

Search

Navigation