4.6 Article

Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal

期刊

DIAGNOSTICS
卷 12, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/diagnostics12051235

关键词

electrocardiogram; automatic sleep scoring; deep learning; convolutional neural network; recurrent neural network; deep convolutional recurrent network

资金

  1. National Information Society Agency (NIA) - Ministry of Science, ICT through the Big Data Platform and Center Construction Project [2022-Data-W18]
  2. Ministry of Education of the Republic of Korea [NRF-2020S1A5A2A03045088]
  3. National Research Foundation of Korea [NRF-2020S1A5A2A03045088]
  4. Medical data-driven hospital support project through the Korea Health Information Service - Ministry of Health & Welfare, Republic of Korea
  5. National Research Foundation of Korea [2020S1A5A2A03045088] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

This study constructed a deep convolutional recurrent (DCR) model based on raw single-lead electrocardiogram (ECG) for the automatic scoring of sleep stages. The DCR model showed superior performance in multiclass and three-class sleep stage classification, and can serve as an alternative tool for sleep monitoring and screening.
Background: Sleep stage scoring, which is an essential step in the quantitative analysis of sleep monitoring, relies on human experts and is therefore subjective and time-consuming; thus, an easy and accurate method is needed for the automatic scoring of sleep stages. Methods: In this study, we constructed a deep convolutional recurrent (DCR) model for the automatic scoring of sleep stages based on a raw single-lead electrocardiogram (ECG). The DCR model uses deep convolutional and recurrent neural networks to apply the complex and cyclic rhythms of human sleep. It consists of three convolutional and two recurrent layers and is optimized by dropout and batch normalization. The constructed DCR model was evaluated using multiclass classification, including five-class sleep stages (wake, N1, N2, N3, and rapid eye movement (REM)) and three-class sleep stages (wake, non-REM (NREM), and REM), using a raw single-lead ECG signal. The single-lead ECG signal was collected from 112 subjects in two groups: control (52 subjects) and sleep apnea (60 subjects). The single-lead ECG signal was preprocessed, segmented at a duration of 30 s, and divided into a training set of 89 subjects and test set of 23 subjects. Results: We achieved an overall accuracy of 74.2% for five classes and 86.4% for three classes. Conclusions: These results show the DCR model's superior performance over those in the previous studies, highlighting that the model can be an alternative tool for sleep monitoring and sleep screening.

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