Sliding window averaging in normal and pathological motor unit action potential trains
Fecha
2018Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
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10.1016/j.clinph.2018.02.134
Resumen
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 MUA ...
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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. [--]
Materias
Electromyography,
Motor unit action potential,
Averaging,
Sliding-window,
MUAP waveform
Editor
Elsevier
Publicado en
Clinical Neurophysiology, 2018, 129(6), 1170-1181
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila
Versión del editor
Entidades Financiadoras
This work has been supported by the Spanish Ministerio de Economía y Competitividad, under the TEC2014-58947-R project.