Validation of empirical mode decomposition combined with notch filtering to extract electrical stimulation artifact from surface electromyograms during functional electrical stimulation

Abstract

This paper presents the validity of Empirical Mode Decomposition (EMD) combined with Notch filtering to remove the electrical stimulation (ES) artifact from surface electromyogram (EMG) data for interpretation of muscle responses during Functional Electrical Stimulation (FES) experiments. We hypothesized that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic mode functions (IMFs) which can be further used to isolate electrical stimulation (ES) artifact. The basic EMD algorithm was used to decompose the ES induced EMG signals into IMFs. IMFs most contaminated by ES were identified based on the standard deviation (SD) criterion. An IMF with the maximum signal to noise ratio (SNR) was Notch filtered and added to IMFs containing pure EMG data to get the filtered EMG signal. The method was tested on 5 able bodied (AB) and 2 spinal cord injured (SCI) participants. The validity of the filtered signal was assessed by normalized root mean squared error (NRMSE) and signal to noise (SNR) ratio values obtained by comparing a clean EMG collected during maximum volitional contraction (MVC) and EMD-Notch filtered signal from the combination of a clean EMG with i) simulated ES and, ii) real ES with no activation generated at different ES amplitudes. The results showed that the EMD-Notch filtering approach was successful, reliable and repeatable in extracting pure muscle responses during ES showing improved values for NRMSE and SNR in both AB and SCI individuals.