Muscle activity detection in pathological, weak and noisy myoelectric signals

The estimation of the on-off timing of the human skeletal muscles during movement is a critical issue in weak and noisy myoelectric signal processing, in motor control studies and in clinical applications. In this study, we used an approach based on the continuous wavelet transform to detect the muscular activity, in muscle with low amplitude and low SNR. The method is based on the calculation of a so-called 'manifestation variable' computed as the maximum output of a bank of matched filters at different scales. EMG signals were generated by simulation using software tool based on an EMG mathematical model and different thresholds were applied for estimating the muscle on- off timing in simulated pathological, weak and noisy (several low SNR values were analyzed) myoelectric signals. The true timing of the EMG activity and the estimated timing of the EMG activity were compared by using a relative percentage error criterion. We performed a two-way ANOVA test, with SNR and threshold as factors, to determine possible significant effects on the relative percentage error. Our results showed that this approach shows satisfactory performances especially when proper threshold values are chosen. In particular, despite the estimated timing of the EMG activity approaches the true timing when SNR is higher, the method works well also for very low SNR. Therefore, this approach to estimate the on-off timing of muscles could be used to study pathological, weak and noisy myoelectric signals.