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Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

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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.

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Acknowledgments

This project was supported by Beijing Natural Science Foundation (Grant Numbers: 3102028, 3122034) and General Logistics Science Foundation (Grant Number: CWS11C108). The work was also funded in part by the National Institute of Biomedical Imaging and Bio-engineering and by the National Institute of General Medical Sciences, under NIH cooperative agreement U01-EB-008577 and NIH grant R01- EB001659.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Chinese PLA (People’s Liberation Army) General Hospital, Beijing, China

    Zhengbo Zhang, Hao Wu & Weidong Wang

  2. Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zhengbo Zhang & Ikaro Silva

  3. Department of Computer Science and Engineering, The University of Tennessee at Chattanooga, Chattanooga, TN, USA

    Dalei Wu

  4. Institute of Medical Equipment, National Biological Protection Engineering Centre, Tianjin, China

    Jiewen Zheng

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  1. Zhengbo Zhang
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  2. Ikaro Silva
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Correspondence to Weidong Wang.

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Zhang, Z., Silva, I., Wu, D. et al. Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems. Med Biol Eng Comput 52, 1019–1030 (2014). https://doi.org/10.1007/s11517-014-1201-7

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