Sliding window averaging in normal and pathological motor unit action potential trains
Keywords: 
Electromyography
Motor unit action potential
Averaging
Sliding-window
MUAP waveform
Issue Date: 
2018
Publisher: 
Elsevier BV
Project: 
info:eu-repo/grantAgreement/MINECO/Retos Investigación: Proyectos de I+D+I (2014)/TEC2014-58947-R/ES/ESTIMACION DE LA ESTRUCTURA DE LA UNIDAD MOTORA A PARTIR DE REGISTROS DE ELECTROMIOGRAFIA DE BARRIDO (SCANNING-EMG) Y ELECTROMIOGRAFIA DE SUPERFICIE DE ALTA DENSIDAD (HD-SEMG)
ISSN: 
1388-2457
Note: 
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Citation: 
Malanda-Trigueros, A. (Armando); Navallas-Irujo, J. (Javier); Rodríguez-Falces, J. (Javier); et al. "Sliding window averaging in normal and pathological motor unit action potential trains". Clinical neurophysiology. 129 (6), 2018, 1170 - 1181
Abstract
Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles. Methods: Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. Results: For signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%). Conclusions: For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. Significance: The new method can be of service in quantitative EMG.

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