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Non-contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory

Non-contact Heartbeat Detection Based on Ballistocardiogram Using UNet and Bidirectional Long Short-Term Memory

Yaozong MaiZizhao ChenBaoxian YuYe LiZhiqiang PangZhang Han


Abstract:
Benefiting from non-invasive sensing technologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 24 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.
Published in: IEEE Journal of Biomedical and Health Informatics ( Early Access )
Page(s): 1 - 1
Date of Publication: 25 March 2022 
ISSN Information:
PubMed ID: 35333727
Funding Agency:
Special Funds for the Cultivation of Guangdong College Students Scientific and Technological Innovation (Grant Numberpdjh2021b0133)
Industry-Academia-Research Innovation Project of Blue-Fire of Ministry of Education (Grant NumberCXZJHZ201803)
10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number2022A1515010104)
Science and Technology Program of Guangzhou (Grant Number202002030353 and 202102021114)
10.13039/501100001809-National Natural Science Foundation of China (Grant NumberU1913210)