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Development and Validation of Algorithms for Sleep Stage Classification and Sleep Apnea/Hypopnea Event Detection Using a Medical-Grade Wearable Physiological Monitoring System

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Wireless Mobile Communication and Healthcare (MobiHealth 2021)

Abstract

Sleep is critical to the overall health of humans. Polysomnography (PSG) is the current gold standard for measuring sleep and diagnosing sleep-related breathing disorders. However, this method is labor-intensive, time-consuming, and confined to a sleep laboratory. In this paper, we leverage algorithms for sleep stage classification and sleep apnea/hypopnea event detection by using signals from single-lead electrocardiograph (ECG) and respiration. To validate the accuracy of the above two algorithms, two independent validation studies were conducted using a medical-grade wearable monitoring system to collect physiological data from patients in both clinical and home settings. In the validation study of sleep stage classification, the average accuracy of our four-class stage classification using the bi-directional long short-term memory (BLSTM) method is 77.83% on our in-house dataset of 30 enrolled patients. In the experiments of sleep apnea screening, the two-level apnea-hypopnea index (AHI) classification reports the overall accuracies of 96.67% and 91.43% in clinical and home environments, respectively. The results showed that the sleep analysis algorithms presented in this paper have good performance in both sleep stage classification and sleep event detection, either in clinical scenario and home settings, indicating that our device can be used along with the two algorithms for sleep analysis.

Zhao Wang and Zhicheng Yang equally contributed to this work.

This work was done during Zhicheng Yang’s internship at Beijing SensEcho Science & Technology Co., Ltd., Beijing, China, when he was a Ph.D. candidate at University of California, Davis, CA, USA.

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Acknowledgment

This work is supported by The National Natural Science Foundation of China (62171471); Beijing Municipal Science and Technology (Z181100001918023); Big Data Research & Development Project of Chinese PLA General Hospital (2018MBD-09).

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Authors and Affiliations

  1. Medical School of Chinese PLA, Beijing, China

    Zhao Wang

  2. PAII Inc., Palo Alto, Santa Clara, CA, USA

    Zhicheng Yang

  3. Beijing SensEcho Science and Technology Co., Ltd., Beijing, China

    Ke Lan & Yanli Hao

  4. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Peiyao Li

  5. Sleep Medicine Division, Airforce Medical Center, Beijing, China

    Ying Duan

  6. Department of Respiratory Medicine, Chinese PLA General Hospital, Beijing, China

    Yingjia She & Yuzhu Li

  7. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China

    Zhengbo Zhang

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  1. Zhao Wang
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Correspondence to Yuzhu Li or Zhengbo Zhang .

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Editors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  2. The University of Sydney, Sydney, NSW, Australia

    Abbas Jamalipour

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Guo

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Wang, Z. et al. (2022). Development and Validation of Algorithms for Sleep Stage Classification and Sleep Apnea/Hypopnea Event Detection Using a Medical-Grade Wearable Physiological Monitoring System. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_12

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  • DOI: https://doi.org/10.1007/978-3-031-06368-8_12

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  • Online ISBN: 978-3-031-06368-8

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