Channel combination analysis for sleep arousal detection based on deep learning method

Donglin Xie; Xingjun Wang; Xiang Gao; Yanru Li

doi:10.1117/12.2688243

28 August 2023 Channel combination analysis for sleep arousal detection based on deep learning method

Donglin Xie, Xingjun Wang, Xiang Gao, Yanru Li

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127240U (2023) https://doi.org/10.1117/12.2688243
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

In this paper, we propose a set of effective channel combinations for detecting sleep arousal based on respiration-related channels and pulse-related channels based on PSG signals and deep learning methods. Different from the single or combined channels of electroencephalogram and electromyography used in most studies related to arousal detection algorithms, this paper inputs four signal channels of thoracic respiratory effort (THO), abdominal respiratory effort (ABD), pulse wave (Pleth) and pulse wave oxygen saturation (SpO2) from clinical PSG data into the neural network classification model, and achieves on the test set with precision 83.34%, recall 86.45%, specificity 99.16% and accuracy 98.57%. The proposed combination of signal channels for arousal detection is in line with the theoretical basis and is an innovative combination of channels, which is also at the leading position of detection performance compared with previous studies and provides ideas for future arousal detection using lightweight wearable devices. This paper is innovative, applicable and generalizable.

Citation Download Citation

Donglin Xie, Xingjun Wang, Xiang Gao, and Yanru Li "Channel combination analysis for sleep arousal detection based on deep learning method", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127240U (28 August 2023); https://doi.org/10.1117/12.2688243

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available