Zhang, J.; Soangra, R.; E. Lockhart, T. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance. Sci2020, 2, 53.
Zhang, J.; Soangra, R.; E. Lockhart, T. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance. Sci 2020, 2, 53.
Zhang, J.; Soangra, R.; E. Lockhart, T. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance. Sci2020, 2, 53.
Zhang, J.; Soangra, R.; E. Lockhart, T. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance. Sci 2020, 2, 53.
Abstract
One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.
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