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Smart technologies toward sleep monitoring at home

Biomedical Engineering Letters volume 9, pages 73–85 (2019)Cite this article

Abstract

With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifies brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep efficiency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep efficiency, respectively, and a correlation coefficient of 0.94 for apnea–hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep efficiency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep efficiency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1A5A1015596) and Samsung Electronics (800-20180337) Co. Ltd.

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080, Korea

    Kwang Suk Park

  2. Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Korea

    Kwang Suk Park & Sang Ho Choi

  3. Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, Korea

    Kwang Suk Park

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Park, K.S., Choi, S.H. Smart technologies toward sleep monitoring at home. Biomed. Eng. Lett. 9, 73–85 (2019). https://doi.org/10.1007/s13534-018-0091-2

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Keywords

  • Sleep monitoring
  • Unconstrained
  • Nonintrusive
  • Polysomnography
  • Ubiquitous healthcare