Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems
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11517_2014_Article_1201.pdf
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Author(s)
Zhang, Zhengbo
Silva, Ikaro
Wu, Dalei
Zheng, Jiewen
Wu, Hao
Wang, Weidong
Date Issued
October 2014
Journal
Medical & Biological Engineering & Computing
Publisher
Springer Berlin Heidelberg
Citation
Zhang, Zhengbo et al. “Adaptive Motion Artefact Reduction in Respiration and ECG Signals for Wearable Healthcare Monitoring Systems.” Medical & Biological Engineering & Computing 52.12 (2014): 1019–1030.
Version
Author's final manuscript
Abstract
Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
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DOI of Published Version
https://doi.org/10.1007/s11517-014-1201-7
