A Data-Driven Volitional EMG Extraction Algorithm During Functional Electrical Stimulation With Time Variant Parameters

Objectives: The goal of this study is to design a novel approach for extracting volitional electromyography (vEMG) contaminated by functional electrical stimulation (FES) with time variant amplitudes and frequencies. Methods: A selective interpolation (SI) is adopted to eliminate the initial spike. Then the interpolated signal is decomposed into intrinsic mode functions by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Each IMF is window-filtered based on a logistic regression (LR) classifier to identify the IMFs contaminated by FES. Semi-simulated signals were generated using EMG and stimulation response and three metrics were adopted to validate the performance of the proposed algorithm, including the a) signal-to-noise ratio (SNR), b) normalized root mean squared error (NRMSE) and c) cross-correlation coefficient between the clean EMG and the extracted EMG. Real FES-contaminated EMG was collected from six able bodied volunteers and one stroke patient. The correlation coefficients between the extracted EMG and the wrist torque were analyzed. Results: The simulation results showed a higher SNR (2.12 to -2.13dB), higher correlation (0.88± 0.08) and lower NRMSE (0.78 to 1.29) than those of the comb filter and EMD-Notch algorithm. The EMG-Torque correlation coefficients were 0.75± 0.07 for monopolar pulses and 0.77± 0.12 for bipolar pulses. For the stroke patient, the algorithm also successfully extracted underlying vEMG from time variant FES noises. Conclusions: All results showed that SICEEMDAN-LR is capable of extracting EMG during FES with time-variant parameters.