A1 Journal article (refereed)
A deep learning model developed for sleep apnea detection : A multi-center study (2023)

Li, F., Xu, Y., Chen, J., Lu, P., Zhang, B., & Cong, F. (2023). A deep learning model developed for sleep apnea detection : A multi-center study. Biomedical Signal Processing and Control, 85, Article 104689. https://doi.org/10.1016/j.bspc.2023.104689

JYU authors or editors

Publication details

All authors or editors: Li, Fan; Xu, Yan; Chen, Junjun; Lu, Ping; Zhang, Bin; Cong, Fengyu

Journal or series: Biomedical Signal Processing and Control

ISSN: 1746-8094

eISSN: 1746-8108

Publication year: 2023

Publication date: 20/03/2023

Volume: 85

Article number: 104689

Publisher: Elsevier BV

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1016/j.bspc.2023.104689

Research data link: https://github.com/Lifanfan-dlut/OSA-detection

Publication open access: Not open

Publication channel open access: 

Abstract

Objective. Manual detection of obstructive sleep apnea (OSA) is a challenging task for many reasons such as time-consuming and subjective. This study aimed to develop a deep learning model for automatically detecting abnormal sleep respiratory episodes in patients with OSA and to explore the performance differences between different sleep centers and different kind of sleep monitoring devices. Methods. The proposed model which comprised branch convolution, separable convolution, and self-attention mechanisms, took minute-to-minute raw electroencephalography (EEG) and oral-nasal airflow signals as its input. Results. The results of five-fold cross-validation in 50 clinical polysomnography recordings evidenced that the average accuracy was 0.877, with a sensitivity and specificity of 0.837, and 0.873, respectively. The proposed method achieved almost perfect agreement with manual scoring method according to the apnea-hypopnea index. In addition, experiments revealed that the combination of EEG and oral-nasal airflow signals could improve the accuracy of approximately 10% relative to oral-nasal airflow signals. Furthermore, a significant difference (P < 0.05) in performance between different sleep centers and different sleep-monitoring devices was also observed. Conclusion and significance. The proposed model achieved a comparably high performance in three different sleep centers, indicating its feasibility and robustness. This study suggested the reliable and simple alternative to clinical OSA screening. The model and data can be found at https://github.com/Lifanfan-dlut/OSA-detection.

Keywords: sleep disorders; sleep apnea syndrome; diagnostics; EEG; machine learning; deep learning

Free keywords: electroencephalography; multi-center; obstructive sleep apnea; deep learning

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 1