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Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network

  • Mobile & Wireless Health
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Abstract

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F1-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

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Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Education. It is a product of the Local Innovative Creative Human Resource Training Project (NRF-2014H1C1A1063845).

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Health Science, Yonsei University, 1, Yeonsedae-gil, Wonju-si, Gangwon-do, 26493, South Korea

    Erdenebayar Urtnasan, Jong-Uk Park & Kyoung-Joung Lee

  2. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Eun-Yeon Joo

Authors
  1. Erdenebayar Urtnasan
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  2. Jong-Uk Park
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  3. Eun-Yeon Joo
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  4. Kyoung-Joung Lee
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Correspondence to Kyoung-Joung Lee.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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This article is part of the Topical Collection on Mobile & Wireless Health

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Urtnasan, E., Park, JU., Joo, EY. et al. Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network. J Med Syst 42, 104 (2018). https://doi.org/10.1007/s10916-018-0963-0

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  • DOI: https://doi.org/10.1007/s10916-018-0963-0

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