Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

Abstract

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.