Extraction of Muscle Anatomical and Physiological Information from Multi-Channel Surface EMG Signals: Applications in Obstetrics

Abstract

Motor Unit (MU) innervation zones (IZs) localization is an important step in several clinical and non-clinical applications including 1) Acquisition of sEMG signal for accurate estimation of its amplitude and other parameters by avoiding placing the electrodes on IZs, 2) Accurate estimation of the EMG-Force relationship, 3) Effective injection of Botulinum Toxin in Post-stroke Spasticity near the IZs, and 4) Guiding the obstetricians to perform episiotomy during child delivery by avoiding cutting near the IZs of External Anal Sphincter (EAS) muscle. The minimal invasive way to identify the location of the IZs generally for any muscle and specifically for EAS muscle is to use multi-channel EMG signals. MU IZs can be detected from the multi-channel sEMG signals, for a fusiform muscle if the signal is acquired with an array of electrodes placed parallel to the muscle fibers, using digital signal and image processing algorithms. As most of the signal processing algorithms work on an adequate quality of the signal, thus before detecting the innervation zone it is made sure that the signal is of good quality. For this purpose, a method based on statistical thresholding of various parameters is proposed to detect the bad channels in the sEMG signals. If the number of the bad consecutive channels are more than 2 then it is suggested to acquire the signal again, otherwise each bad channel is approximated by the interpolation of its neighbor channels. As some background noise is always acquired with the EMG signal so further image enhancement techniques are used to enhance the MUAP propagation region in the spatio-temporal images and suppress the background noise. The MUAP pattern is then detected in the spatio-temporal sEMG images using multi-scale Hessian based filtering and the corresponding MU IZs are identified as the starting point of propagation of the MUAP. A software is also developed which can be used to visualize the signals acquired from EAS, detect and display the IZs and more importantly compute and display the histogram of the IZs and generate reports which will help the obstetrician while performing episiotomy during child delivery to avoid cutting vulnerable regions that may lead to fecal incontinence at later age.