Effects of Electromyogram Signal filtering on Muscle Activation Time

Important information gained using dynamic electromyography is to accurately define the muscle action and phase timing within the gait cycle. Human gait relies on selective timing and intensity of appropriate muscle activations for stability, loading and progression over the supporting foot during stance, and further to advance the limb in swing phase. A traditional clinical practice is to low pass filter the integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. The accuracy of defining period of significant muscle activations by EMG varies with temporal shift involved in filtering of the signals. Low pass filtering with fixed order and cut-off frequency will introduce delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine onset and cessation times of the muscles, we explore onset and cessation epochs with denoised EMG signals using wavelets denoising, Empirical mode decomposition (EMD) and Ensemble empirical mode decomposition (EEMD) method, which are considered suitable tools for analyzing nonlinear and non-stationary signals such as EMG. Gastrocnemius muscle onset and cessation were determined in eight participants with two different walking conditions. Low pass filtering of integrated EMG (iEMG) signals resulted in premature onset (about 28% of stance duration) in younger when compared with iEMG signals. We also found significantly different onset time events (p<0.02 for normal speed walking, p<0.01 for fast speed walking) between those detected by low pass filtering and iEMG signals. Wavelet denoising accurately predicted onsets for normal walking. EEMD denoised signals could further detect preactivation onsets during fast walking condition.