AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

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

Ting Cao, Zhiwei Lian( ), Heng Du, Jingyun Shen, Yilun Fan, Junmeng Lyu
School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Show Author Information

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.

Keywords

sleep environmentsleep stagingmodelphysiological signalsmachine learningenvironmental control

Electronic Supplementary Material

Download File(s)
bs-16-8-1409_ESM.pdf (940 KB)

References

 

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.
Crossref Google Scholar
 

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

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

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

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

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.
Crossref 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.
Crossref Google Scholar
 

Lan L, Tsuzuki K, Liu YF, et al. (2017). Thermal environment and sleep quality: A review. Energy and Buildings, 149: 101–113.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
 
Roffo G, Melzi S, Cristani M (2015). Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.
Crossref
 

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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
 

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

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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
Building Simulation
Volume 16 Issue 8,
August 2023
Pages 1409-1423
DOI: 10.1007/s12273-023-1049-6
Cite this article:
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

4176

Views

2

Crossref

2

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 10 April 2023
Revised: 09 May 2023
Accepted: 24 May 2023
Published: 10 July 2023
© Tsinghua University Press 2023
Return