TY - GEN
T1 - GANs-based Signal Quality Assessment for Heart Rate Estimation with Ballistocardiograph
AU - Cai, Ruilin
AU - Qi, Jun
AU - Li, Jiayi
AU - Chen, Jianjun
AU - Wang, Wei
AU - Zhang, Haiyang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The ballistocardiograph (BCG) is a non-contact technology that monitors the heart and provides detailed cardiovascular parameters. Despite its broad applicability for long-term home monitoring due to Covid-19, BCG signals face challenges from positional changes, body movements, and system noise, which impact detection algorithms. In this paper, we propose a method for detecting inter-beat intervals (IBI) based on signal fusion technology. We utilize a Dynamic Bayesian Network (DBN) to integrate five heartbeat localization features extracted from BCG signals. Additionally, Generative Adversarial Networks (GANs) are used to assess signal quality and select correlated channels, improving heart rate monitoring accuracy. Experimental results demonstrate an average coverage of 95.21% and a mean squared error of 0.05. These results outperform those of methods without channel selection and single-channel BCG, indicating the potential for improving IBI estimation in multichannel BCG signal sensor systems.
AB - The ballistocardiograph (BCG) is a non-contact technology that monitors the heart and provides detailed cardiovascular parameters. Despite its broad applicability for long-term home monitoring due to Covid-19, BCG signals face challenges from positional changes, body movements, and system noise, which impact detection algorithms. In this paper, we propose a method for detecting inter-beat intervals (IBI) based on signal fusion technology. We utilize a Dynamic Bayesian Network (DBN) to integrate five heartbeat localization features extracted from BCG signals. Additionally, Generative Adversarial Networks (GANs) are used to assess signal quality and select correlated channels, improving heart rate monitoring accuracy. Experimental results demonstrate an average coverage of 95.21% and a mean squared error of 0.05. These results outperform those of methods without channel selection and single-channel BCG, indicating the potential for improving IBI estimation in multichannel BCG signal sensor systems.
KW - Ballistocardiograph
KW - Bayesian network
KW - Generative Adversarial Networks
KW - Multi-channel fusion
UR - http://www.scopus.com/inward/record.url?scp=105000112275&partnerID=8YFLogxK
U2 - 10.1109/ISPA63168.2024.00134
DO - 10.1109/ISPA63168.2024.00134
M3 - Conference Proceeding
AN - SCOPUS:105000112275
T3 - Proceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024
SP - 1014
EP - 1020
BT - Proceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024
Y2 - 30 October 2024 through 2 November 2024
ER -