Accurate EMG onset detection in pathological, weak and noisy myoelectric signals

In this paper, we propose an alternative onset detection method dealing with pathological, weak and noisy myoelectric signals. We evaluate our method on simulated, offline EMG signals, which are supposed to be generated from a relatively small number of motor units (MU's) with various muscle contraction levels and pathological characteristics. These simulated signals were scaled and then superimposed to a standard white noise to obtain various signal conditions (signal noise ratio, SNR). We utilize the Teager-Kaiser Energy (TKE) operator as a fore-processing procedure to highlight amplitude variation on the onset point, and employ two image enhancement technologies, namely, morphological close operator (MCO) and morphological open operator (MOO), as successive post-processing procedures to filter out onset artefacts. A synthesized index for evaluating the method is proposed, which can optimize the parameters according to specific signal conditions. Comparing with other approaches, our method is simple and competitive in accuracy and reliability, especially for the pathological EMG signals in low SNR's. Result on clinic EMG signals that collected from healthy subjects and patients with amyotrophic lateral sclerosis and myopathy also verifies our design.