Separation of interference surface electromyogram into propagating and non-propagating components. (July 2019)

• Record Type: Journal Article
• Authors: Mesin, Luca
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 238
• Page End: 247
• Publication Date: 2019-07
• Subjects: CV muscle fiber conduction velocity -- DD double differential filter -- EMG electromyogram -- FD fractal dimension -- IED inter-electrode distance -- IZ innervation zone -- MU motor unit -- MUAP motor unit action potential -- MVC maximum voluntary contraction -- PSD power spectral density -- RMS root mean square -- SD single differential filter -- SFAP single fiber action potential -- SNR signal to noise ratio; Conduction velocity -- End-of-fiber effect -- Interference EMG -- Linear electrode array; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.04.016
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new algorithm is proposed to decompose surface EMG into propagating and non-propagating components. Propagating component provides accurate information on CV; non-propagating component reflects MU firing statistics. Potential future applications in the study of muscle fatigue and coherence. Abstract: A new algorithm is introduced to decompose interference surface electromyogram (EMG) recorded by a multi-channel system aligned to muscle fibers into propagating and non-propagating contributions. Muscle fiber conduction velocity (CV) is also estimated, reducing the bias induced by non-propagating components. The algorithm is fast and stable, as it is based on alignment and averaging procedures. Simulated signals (with different fat thickness, SNR, number of channels, epoch duration and force level) are used to test the algorithm. The median cross-correlation of simulated and estimated components were about 98% and 90%, for propagating and non-propagating terms, respectively. CV was estimated better than using a multi-channel maximum likelihood approach applied to double differential data (mean error of 0.08 versus 0.13 m/s), with a greater gain in case of thinner fat layer, low SNR and few channels. Example applications to experimental data are also shown (single motor unit action potential, M-wave and interference EMG). Propagating components reflect the travelling of action potentials along muscle fibers. Preliminary tests show that non-propagating contributions provide selective information on motor units firing statistics. The separation of interference EMG into propagating and non-propagating components opens new perspectives, e.g., in the study of synergies, common drive and myoelectric manifestations of fatigue.