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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
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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

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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

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