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Comparative Study
. 2020 Mar 7;20(5):1468.
doi: 10.3390/s20051468.

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Xiang An  1 , George K Stylios  1
Affiliations
Comparative Study

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Xiang An et al. Sensors (Basel). .

Abstract

A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

Keywords: adaptive filtering; electrocardiogram; impedance pneumography signal; motion artefact.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The designed finite impulse response (FIR) high-pass filter: (a) magnitude response (blue) and phase response (red); (b) group delay.
Figure 2
Figure 2
The designed infinite impulse response (IIR) high-pass filter: (a) magnitude response (blue) and phase response (red); (b) group delay.
Figure 3
Figure 3
Comparison of conventional and zero-phase infinite impulse response (IIR) filtering.
Figure 4
Figure 4
A demonstration of 3-level discrete wavelet transform (DWT) decomposition.
Figure 5
Figure 5
Principle of the adaptive filter in noise cancellation.
Figure 6
Figure 6
Experimental signals: (a) normal electrocardiogram (ECG) from the MIT-BIH database “mitdb/106”; (b) baseline wander noise from the MIT-BIH database “nstdb/bw”; (c) noisy ECG signal.
Figure 7
Figure 7
Experimental signals: (a) abnormal ECG from the MIT-BIH database “mitdb/106” where the premature ventricle contraction and ventricle couplet happens; (b) baseline wander noise from the MIT-BIH database “nstdb/bw”; (c) noisy ECG signal.
Figure 8
Figure 8
Denoised normal ECG waveform by different noise reduction methods: (a) FIR filter; (b) IIR filter; (c) moving average filter; (d) moving median filter; (e) wavelet transform denoising; (f) empirical mode decomposition (EMD); (g) adaptive filter.
Figure 8
Figure 8
Denoised normal ECG waveform by different noise reduction methods: (a) FIR filter; (b) IIR filter; (c) moving average filter; (d) moving median filter; (e) wavelet transform denoising; (f) empirical mode decomposition (EMD); (g) adaptive filter.
Figure 8
Figure 8
Denoised normal ECG waveform by different noise reduction methods: (a) FIR filter; (b) IIR filter; (c) moving average filter; (d) moving median filter; (e) wavelet transform denoising; (f) empirical mode decomposition (EMD); (g) adaptive filter.
Figure 9
Figure 9
Denoised abnormal ECG waveform by different noise reduction methods: (a) FIR filter; (b) IIR filter; (c) moving average filter; (d) moving median filter; (e) wavelet transform denoising; (f) EMD; (g) adaptive filter.
Figure 9
Figure 9
Denoised abnormal ECG waveform by different noise reduction methods: (a) FIR filter; (b) IIR filter; (c) moving average filter; (d) moving median filter; (e) wavelet transform denoising; (f) EMD; (g) adaptive filter.
Figure 10
Figure 10
The principal of impedance pneumography (IP) measurement.
Figure 11
Figure 11
The IP signal consisting of respiratory signal and motion artefacts.
Figure 12
Figure 12
Hardware configuration.
Figure 13
Figure 13
The analog front-end (ECG + IP functions).
Figure 14
Figure 14
(a) The arranged textile electrodes, (b) the measurement of the ECG and IP signals.
Figure 15
Figure 15
(a) The motion artefact-contaminated ECG and IP signals; (b) the low-pass filtered and normalized ECG and IP signals.
Figure 16
Figure 16
The denoised ECG signal by the adaptive filter.
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