Building Simulation 2023, 16(8): 1409-1423 https://doi.org/10.1007/s12273-023-1049-6

Research Article | Issue | Published: 10 July 2023

A sleep staging model for the sleep environment control based on machine learning

Show Author's Information Hide Author's Information Ting Cao, Zhiwei Lian(

), Heng Du, Jingyun Shen, Yilun Fan, Junmeng Lyu

School of Design, Shanghai Jiao Tong University, Shanghai 200240, China

Keywords:

machine learning, model, sleep environment, sleep staging, physiological signals, environmental control

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Cao T, Lian Z, Du H, et al. A sleep staging model for the sleep environment control based on machine learning. Building Simulation, 2023, 16(8): 1409-1423. https://doi.org/10.1007/s12273-023-1049-6

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Abstract Full text Electronic supplementary material About this article

Abstract

To date, dynamic sleep environment has been attracted the focus of researchers. Owing to the individual difference on sleep phase and thermal comfort, changes in sleep environment should be occupant-centered, and precise regulation of the environment required current sleep stages. However, few studies connected occupants and the environment through physiological signal-based model of sleep staging. Therefore, this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information. Raw database was processed and selected efficiently according to the characteristics of physiological signals. Finally, the sleep staging model with an average accuracy of 93.9% was built, and other mean indicators (precision: 82.5%, recall: 83.1%, F1 score: 82.8%) performed well. The features adopted by model were found to come from different brain regions, and the global brain signals were suggested to play an important role in the construction of sleep staging model. Moreover, the computational processing of physiology signals should consider their characteristics, i.e., time domain, frequency domain, time-frequency domain and nonlinear characteristics. The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.

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About this article

A sleep staging model for the sleep environment control based on machine learning

Show Author's information Hide Author's Information Ting Cao, Zhiwei Lian(

), Heng Du, Jingyun Shen, Yilun Fan, Junmeng Lyu

School of Design, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract

Keywords: machine learning, model, sleep environment, sleep staging, physiological signals, environmental control

References(57)

Acharya UR, Faust O, Kannathal N, et al. (2005). Non-linear analysis of EEG signals at various sleep stages. Computer Methods and Programs in Biomedicine, 80: 37–45.

DOI Google Scholar

Alickovic E, Subasi A (2018). Ensemble SVM method for automatic sleep stage classification. IEEE Transactions on Instrumentation and Measurement, 67: 1258–1265.

DOI Google Scholar

Berry RB, Quan SF, Abreu AR, et al. (2020). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version 2.6. Darien, IL, USA: American Academy of Sleep Medicine.

Bersagliere A, Achermann P (2010). Slow oscillations in human non-rapid eye movement sleep electroencephalogram: Effects of increased sleep pressure. Journal of Sleep Research, 19: 228–237.

DOI Google Scholar

Bresch E, Großekathöfer U, Garcia-Molina G (2018). Recurrent deep neural networks for real-time sleep stage classification from single channel EEG. Frontiers in Computational Neuroscience, 12: 85.

DOI Google Scholar

Cao T, Lian Z, Zhu J, et al. (2022). Parametric study on the sleep thermal environment. Building Simulation, 15: 885–898.

DOI Google Scholar

Carl C, Açık A, König P, et al. (2012). The saccadic spike artifact in MEG. NeuroImage, 59: 1657–1667.

DOI Google Scholar

Craik A, He Y, Contreras-Vidal JL (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16: 031001.

DOI Google Scholar

Ding C, Peng H (2003). Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the 2003 IEEE Bioinformatics Conference, Stanford, CA, USA.

Fan C, Chen M, Tang R, et al. (2022). A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Building Simulation, 15: 197–211.

DOI Google Scholar

Fell J, Röschke J, Mann K, et al. (1996). Discrimination of sleep stages: A comparison between spectral and nonlinear EEG measures. Electroencephalography and Clinical Neurophysiology, 98: 401–410.

DOI Google Scholar

Fleiss JL, Cohen J (1973). The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 33: 613–619.

DOI Google Scholar

Fu M, Wang Y, Chen Z, et al. (2021). Deep learning in automatic sleep staging with a single channel electroencephalography. Frontiers in Physiology, 12: 628502.

DOI Google Scholar

Gan VJL, Wang B, Chan CM, et al. (2022). Physics-based, data-driven approach for predicting natural ventilation of residential high-rise buildings. Building Simulation, 15: 129–148.

DOI Google Scholar

Ghahramani A, Castro G, Karvigh SA, et al. (2018). Towards unsupervised learning of thermal comfort using infrared thermography. Applied Energy, 211: 41–49.

DOI Google Scholar

Gharbali AA, Najdi S, Fonseca JM (2018). Investigating the contribution of distance-based features to automatic sleep stage classification. Computers in Biology and Medicine, 96: 8–23.

DOI Google Scholar

Grossi E, Valbusa G, Buscema M (2021). Detection of an autism EEG signature from only two EEG channels through features extraction and advanced machine learning analysis. Clinical EEG and Neuroscience, 52: 330–337.

DOI Google Scholar

Haskell EH, Palca JW, Walker JM, et al. (1981). The effects of high and low ambient temperatures on human sleep stages. Electroencephalography and Clinical Neurophysiology, 51: 494–501.

DOI Google Scholar

Hassan AR, Bhuiyan MIH (2016). A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. Journal of Neuroscience Methods, 271: 107–118.

DOI Google Scholar

Hassan AR, Subasi A (2017). A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowledge-Based Systems, 128: 115–124.

DOI Google Scholar

Imtiaz SA, Rodriguez-Villegas E (2014). A low computational cost algorithm for REM sleep detection using single channel EEG. Annals of Biomedical Engineering, 42: 2344–2359.

DOI Google Scholar

Jaffal I (2023). Physics-informed machine learning for metamodeling thermal comfort in non-air-conditioned buildings. Building Simulation, 16: 299–316.

DOI Google Scholar

Kononenko I (1994). Estimating attributes: Analysis and extensions of RELIEF. In: Bergadano F, De Raedt L (eds), Machine Learning: ECML-94. Berlin, Heidelberg: Springer.

DOI

Lan L, Lian Z (2010). Application of statistical power analysis - How to determine the right sample size in human health, comfort and productivity research. Building and Environment, 45: 1202–1213.

DOI Google Scholar

Lan L, Lian ZW, Lin YB (2016). Comfortably cool bedroom environment during the initial phase of the sleeping period delays the onset of sleep in summer. Building and Environment, 103: 36–43.

DOI Google Scholar

Lan L, Tsuzuki K, Liu YF, et al. (2017). Thermal environment and sleep quality: A review. Energy and Buildings, 149: 101–113.

DOI Google Scholar

Lomas T, Ivtzan I, Fu CHY (2015). A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience & Biobehavioral Reviews, 57: 401–410.

DOI Google Scholar

Louis RP, Lee J, Stephenson R (2004). Design and validation of a computer-based sleep-scoring algorithm. Journal of Neuroscience Methods, 133: 71–80.

DOI Google Scholar

Magosso E, Provini F, Montagna P, et al. (2006). A wavelet based method for automatic detection of slow eye movements: A pilot study. Medical Engineering & Physics, 28: 860–875.

DOI Google Scholar

Malafeev A, Laptev D, Bauer S, et al. (2018). Automatic human sleep stage scoring using deep neural networks. Frontiers in Neuroscience, 12: 781.

DOI Google Scholar

Miyake S, Sato N, Akatsu J, et al. (1996). The effects of fluctuating room temperature on night-sleep in human. The Japanese Journal of Ergonomics, 32: 239–249.

DOI Google Scholar

Morselli LL, Temple KA, Leproult R, et al. (2018). Determinants of slow-wave activity in overweight and obese adults: roles of sex, obstructive sleep apnea and testosterone levels. Frontiers in Endocrinology, 9: 377.

DOI Google Scholar

Motamedi-Fakhr S, Moshrefi-Torbati M, Hill M, et al. (2014). Signal processing techniques applied to human sleep EEG signals—A review. Biomedical Signal Processing and Control, 10: 21–33.

DOI Google Scholar

Mourtazaev MS, Kemp B, Zwinderman AH, et al. (1995). Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep, 18: 557–564.

DOI Google Scholar

Ngarambe J, Yun G, Lee K, et al. (2019). Effects of changing air temperature at different sleep stages on the subjective evaluation of sleep quality. Sustainability, 11: 1417.

DOI Google Scholar

Oropesa E, Cycon HL, Jobert M (1999). Sleep stage classification using wavelet transform and neural network. International Computer Science Institute.

Google Scholar

Pan ST, Kuo C, Zeng J, et al. (2012). A transition-constrained discrete hidden Markov model for automatic sleep staging. Biomedical Engineering Online, 11: 52.

DOI Google Scholar

Pincus SM (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88: 2297–2301.

DOI Google Scholar

Pop-Jordanova N, Pop-Jordanov J (2005). Spectrum-weighted EEG frequency (“brain-rate”) as a quantitative indicator of mental arousal. Prilozi, 26: 35–42.

Google Scholar

Rechtschaffen A, Kales A (1968). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. US Department of Health, Education, and Welfare; National Institutes of Health.

Richman JS, Moorman JR (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology Heart and Circulatory Physiology, 278: H2039–H2049.

DOI Google Scholar

Roffo G, Melzi S, Cristani M (2015). Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.

DOI

Rosso OA, Blanco S, Yordanova J, et al. (2001). Wavelet entropy: A new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods, 105: 65–75.

DOI Google Scholar

Şen B, Peker M, Çavuşoğlu A, et al. (2014). A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. Journal of Medical Systems, 38: 18.

DOI Google Scholar

Stanus E, Lacroix B, Kerkhofs M, et al. (1987). Automated sleep scoring: a comparative reliability study of two algorithms. Electroencephalography and Clinical Neurophysiology, 66: 448–456.

DOI Google Scholar

Sun C, Chen C, Li W, et al. (2020). A hierarchical neural network for sleep stage classification based on comprehensive feature learning and multi-flow sequence learning. IEEE Journal of Biomedical and Health Informatics, 24: 1351–1366.

DOI Google Scholar

Supratak A, Dong H, Wu C, et al. (2017). DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25: 1998–2008.

DOI Google Scholar

Šušmáková K, Krakovská A (2008). Discrimination ability of individual measures used in sleep stages classification. Artificial Intelligence in Medicine, 44: 261–277.

DOI Google Scholar

Tang R, Fan C, Zeng F, et al. (2022). Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression. Building Simulation, 15: 317–331.

DOI Google Scholar

Vilamala A, Madsen KH, Hansen LK (2017). Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. In: Proceedings of 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

DOI

Wu Y, Cao B, Hu M, et al. (2023). Development of personal comfort model and its use in the control of air conditioner. Energy and Buildings, 285: 112900.

DOI Google Scholar

Xu X, Zhu J, Chen C, et al. (2022). Application potential of skin temperature for sleep-wake classification. Energy and Buildings, 266: 112137.

DOI Google Scholar

Yang T, Bandyopadhyay A, O’Neill Z, et al. (2022). From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control. Building Simulation, 15: 913–932.

DOI Google Scholar

Zhang J, Yao R, Ge W, et al. (2020). Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Computer Methods and Programs in Biomedicine, 183: 105089.

DOI Google Scholar

Zhang N, Cao B, Zhu Y (2023). An effective method to determine bedding system insulation based on measured data. Building Simulation, 16: 121–132.

DOI Google Scholar

Zhao D, Wang Y, Wang Q, et al. (2019). Comparative analysis of different characteristics of automatic sleep stages. Computer Methods and Programs in Biomedicine, 175: 53–72.

DOI Google Scholar

Zhou X, Xu L, Zhang J, et al. (2022). Development of data-driven thermal sensation prediction model using quality-controlled databases. Building Simulation, 15: 2111–2125.

DOI Google Scholar

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Acknowledgements

Publication history

Received: 10 April 2023

Revised: 09 May 2023

Accepted: 24 May 2023

Published: 10 July 2023

Issue date: August 2023

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Acknowledgements

This work was supported by the National Key R&D Program of China (2022YFC3803201) and the National Natural Science Foundation of China (52078291).