GANs-based Signal Quality Assessment for Heart Rate Estimation with Ballistocardiograph

Ruilin Cai, Jun Qi*, Jiayi Li, Jianjun Chen, Wei Wang, Haiyang Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1014-1020
Number of pages7
ISBN (Electronic)9798331509712
DOIs
Publication statusPublished - 2024
Event22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024 - Kaifeng, China
Duration: 30 Oct 20242 Nov 2024

Publication series

NameProceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

Conference

Conference22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024
Country/TerritoryChina
CityKaifeng
Period30/10/242/11/24

Keywords

  • Ballistocardiograph
  • Bayesian network
  • Generative Adversarial Networks
  • Multi-channel fusion

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