[en] Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.
Disciplines :
Neurosciences & behavior
Author, co-author :
Lee, Minji ; The Catholic University of Korea, Department of Biomedical Software Engineering, Bucheon, South Korea
Kang, Hyeokmook; Hanwha Systems, Soldier Combat Research and Development Team, Land Combat System Center, Seoul, South Korea
Yu, Seong-Hyun; Chungbuk National University, Department of Computer Science, Cheongju, South Korea
Cho, Heeseung ; Korea University, Department of Artificial Intelligence, Seoul, South Korea
Oh, Junhyoung; Seoul Women's University, Division of Information Security, Seoul, South Korea
NRF - National Research Foundation of Korea CBNU - Chungbuk National University
Funding text :
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by Korean Government [Ministry of Science and ICT (MSIT)] (Artificial Intelligence Innovation Hub) under Grant RS-2021-II212068; in part by the National Research Foundation of Korea (NRF) grant funded by Korean Government (MSIT) under Grant RS-2023-00252624 and Grant RS-2024-00336880; and in part by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Agriculture and Food Convergence Technologies Program for Research Manpower Development Program funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) under Grant RS-2024-00398561.
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