METHODS article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1608725
This article is part of the Research TopicDigital Technologies in Chronic Disease Management: Strategies for Enhanced PreventionView all 5 articles
Enhancing Sleep Stage Classification with Ballistocardiogram Signals: Feature Selection Using Attention Mechanism and XGBoost
Provisionally accepted
- 1Huzhou University, Huzhou, China
- 2Zhejiang Shuren University, Hangzhou, Zhejiang Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Traditional sleep staging using contact sensors may compromise data validity. This study proposes a non-contact ballistocardiogram (BCG)-based method to improve both accuracy and comfort in sleep monitoring. BCG signals were processed using continuous wavelet transform and low-pass filtering to extract heart rate variability (HRV) and respiratory rate variability (RRV). A novel feature selection model integrating attention mechanisms with XGBoost was developed, where attention weights are used to prioritize features before iterative refinement by XGBoost. Evaluated on 10,201 sleep segments, the Fast-ABC Boost model achieved an accuracy of 89.85%, along with superior precision, recall, F1-score, and Kappa values compared to conventional methods. The attention-XGBoost fusion effectively mitigates interference from noisy and redundant features while optimizing feature relevance, demonstrating robust adaptability to the complexity of sleep signals.This innovation advances the accuracy non-contact sleep staging, enabling practical applications in home healthcare and personalized sleep management, while improving user comfort.
Keywords: Sleep staging, Ballistocardiogram (BCG), heart rate variability (HRV), Respiratory rate variability (RRV), XGBoost, Feature Selection
Received: 09 Apr 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Luo, Liu, Chai and Teng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Banteng Liu, Zhejiang Shuren University, Hangzhou, Zhejiang Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.