Pre-sleep anxiety detection by multimodal signal

Banghao Cai; Miao Liu; Binwen Li; Jiayu Li

doi:10.1117/12.3036776

13 July 2024 Pre-sleep anxiety detection by multimodal signal

Banghao Cai, Miao Liu, Binwen Li, Jiayu Li

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132081P (2024) https://doi.org/10.1117/12.3036776
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

Anxiety significantly impacts individuals' lives. This study employs a machine learning framework to detect anxiety among college students in their daily environments, focusing particularly on pre-sleep anxiety. Data encompassing electrocardiogram (ECG) and triple-axis acceleration (T-ACC) were gathered from 45 college students across 254 days, and calculated the continuous RR intervals in the ECG data. Subsequently, 30 linear and non-linear RR parameters, along with T-ACC parameters, were extracted to form the physiological model features indicative of anxiety. Feature selection was conducted using Sequential Forward Selection and Leave-One-Out Cross Validation methods. A binary classification model was then developed using these selected features through a classical machine learning algorithm, achieving an optimal accuracy of 68.42%. Statistical analysis of the features indicated that anxiety elevates parasympathetic nervous activity and significantly influences heart rate variability on certain features.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Banghao Cai, Miao Liu, Binwen Li, and Jiayu Li "Pre-sleep anxiety detection by multimodal signal", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132081P (13 July 2024); https://doi.org/10.1117/12.3036776

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