Abstract:
Electromyogram (EMG) signal is often contaminated with noise and artifacts of different
sources. These negatively affect the classification and recognition of motion and gestures.
Hence, denoising the signal prior to further processing is crucial for better EMG
applicability. Different denoising techniques have been proposed in the past which are
based on either wavelet transform, blind source separation, or mode decomposition
techniques; however, improvement in these techniques is warranted nonetheless. In this
paper, a novel approach for noise and muscle artifact removal is proposed, employing a
hybrid framework combining variational mode decomposition (VMD) with canonical
correlation analysis (CCA). The proposed framework uses VMD to decompose the signal
into intrinsic mode functions (IMFs) and subsequently leverages CCA to isolate and refine
the noisy IMFs prior to signal reconstruction. The framework outperforms state-of-the-art
EMG denoising techniques like EMD and VMD. The framework was tested on multisubject data acquired for multiple motions. Experimental results show significant
improvements in signal quality, evaluated using signal-to-noise ratio (SNR), percentageroot-mean-square difference (PRD), and root mean square error (RMSE) metrics. This
approach is an effective signal processing tool especially for post-acquisition analysis in
medical diagnostics and research.
