Motion artefact removals for wearable ECG using stationary wavelet transform

Wearable Electrocardiogram (ECG) is attracting much attention in daily healthcare applications. From the viewpoint of long-term use, it is desired that the electrodes are non-contact with the human body. In this study, the authors propose an algorithm using the stationary wavelet transform (SWT) to remove motion artefact superimposed on ECG signal when using non-contact capacitively coupling electrodes. The authors evaluate the effect on motion artefact removal of this algorithm by applying it to various ECG signals with motion artefacts superimposed. As a result, the correlation coefficients of ECG signals with respect to the clean ones have been improved from 0.71 to 0.88 on median before and after motion artefact removal, which demonstrates the validity of the proposed SWT-based algorithm.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  Y. Kishimoto,et al.  Detecting Motion Artifact ECG Noise During Sleeping by Means of a Tri-axis Accelerometer , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Jianqing Wang,et al.  Performance of human body communication-based wearable ECG with capacitive coupling electrodes. , 2016, Healthcare technology letters.

[4]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

[5]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[6]  Jianqing Wang,et al.  Wearable ECG Based on Impulse-Radio-Type Human Body Communication , 2016, IEEE Transactions on Biomedical Engineering.

[7]  Michael Muma,et al.  Motion artifact removal in ECG signals using multi-resolution thresholding , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[8]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..