Muscle Innervation Zone Localization: A Linearity Measure-Based Approach With Applications to Post-Stroke Plasticity and Episiotomy

Abstract

Localization of motor unit (MU) innervation zones (IZs) is an important step in several clinical and non-clinical applications, such as 1) acquisition of surface electromyogram (sEMG) signal for accurate estimation of its amplitude and other parameters by avoiding placing 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 obstetricians to perform episiotomy during child delivery by avoiding cutting near the IZs of External Anal Sphincter (EAS) muscle. The most minimally invasive way to identify the location of motor unit innervation zones (IZs) in any muscle, including the External Anal Sphincter (EAS) muscle, is to use multi-channel surface electromyography (sEMG) signals. In this manuscript, we propose a novel approach for automatic muscle motor unit innervation zone localization using multi-channel electromyography (EMG) signals. Our method is based on a linearity measure derived from eigenvalues of the Hessian matrix. The motor unit action potential (MUAP) propagation pattern is first detected in the spatio-temporal sEMG images using a Linearity measure based on eigenvalues of the Hessian matrix. The corresponding MU IZs is then identified as the starting point of propagation of the MUAP. A software is also developed which can be used to record and visualize the signals acquired from EAS and other muscles, 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. The evaluations,on both simulated and experimental EMG signals, demonstrate the effectiveness and robustness of our proposed approach. Compared to existing methods, our approach achieves higher accuracy in innervation zone localization on experimental and simulated signals with mean absolute error of 0.53 and 0.53 inter electrode distance (IED).

Keywords

• EMG signals
• spatio-temporal image
• eigenvalue
• Hessian matrix
• linearity measure
• innervation zone