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

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.

Human Body–Electrode Interfaces for Wide-Frequency Sensing and Communication: A Review

“…Motion Artefact: The movement of the human body during respiration, while talking or while being stressed, can cause the skin near the electrodes to stretch/deform, resulting in time-varying mismatches in contact potentials [ 88 , 89 ]. These mismatches act as a source of noise and interference in biopotential signal measurements known as motion artefacts.…”

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

“…Several methods have been proposed for ECG enhancement such as independent component analysis (ICA) [187], advanced averaging [188], [189], adaptive filtering [190], SVD [191], maximally decimated perfectreconstruction FIR filter banks [192], wavelet transform [193], [194], and nonlinear filter banks [195]. Generally, one of the foremost challenges in the ECG-based biometric system is the separation of the desired signal from several types of noise such as baseline wander, power line interference, motion artifacts, muscle noise, and other interference [196], [197].…”

“…(a) Baseline wander: Baseline wander is a slow-varying artifact [196], which essentially results from the skinelectrode impedance variation that emerges in the form of a low-frequency noise merged with the ECG signal [198]. Impedance variation can manifest as a result of the individual breath, the electrode-skin contact, and smooth movements [197].…”

“…The authors in [200] proposed the application of discrete wavelet transform (DWT) for noise and wandering suppression in ECG signals, which enable inspecting high-frequency situations of short duration in nonstationary signals. Some researchers have also adopted EMD for baseline wander reduction [196], [201]- [203], [205], which requires a priori knowledge of baseline wander behavior. It is shown in [145] [207].…”

“…However, in regards to its sensitivity variation (caused by impedance variation), it is challenging to acquire the ECG signal due to the motion of the subject. There are several studies on overcoming motion artifacts by addressing it as mere sinusoidal noise in the conventional ECG electrode [13], [196], [214], [215]. Over recent decades, conventional signal-processing methods have been used to overcome motion artifact.…”