Suppression of Motion Artifacts in Multichannel Mechanomyography Using Multivariate Empirical Mode Decomposition

Abstract

The measurements of mechanomyography (MMG) are commonly contaminated by motion artifacts due to limb movement, and artifacts are unavoidable when an acceleration (ACC) sensor is used in dynamic conditions. Conventionally, the Fourier-based approach is used to eliminate the artifacts from the single-channel MMG signal by applying a Butterworth bandpass filter. A noise-assisted multivariate empirical mode decomposition (NA-MEMD) approach is proposed for the suppression of motion artifacts in multichannel MMG signals. The sample autocorrelation and the energy of each intrinsic mode function (IMF) were analyzed to identify the noise-IMF and artifact-IMF, respectively. We describe the proposed method and compare its performance with that of other conventional approaches (Butterworth filter, wavelet denoising) for the multichannel MMG signals recorded from both isometric and dynamic muscle contractions. The experimental results reveal the superiority of the proposed approach due to a lower level of distortion introduced in the MMG data after the suppression of motion artifacts. In addition, MEMD provides a novel basis for MMG signal feature extraction as well as frequency and crosstalk investigations.