Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques

• Published in: Biomedical engineering online Vol. 14; no. 1; p. 84
• Main Authors: Soares, Fabiano Araujo, Carvalho, João Luiz Azevedo, Miosso, Cristiano Jacques, de Andrade, Marcelino Monteiro, da Rocha, Adson Ferreira
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 17.09.2015; BioMed Central
• Subjects: Action Potentials; Adult; Algorithms; Electrodes; Electromyography; Female; Humans; Image Processing, Computer-Assisted; Male; Motor Neurons - cytology; Muscle Contraction - physiology; Muscle Fibers, Skeletal - physiology; Neural Conduction; Signal Processing, Computer-Assisted; Young Adult; Iraq
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-015-0079-4

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.