Sliding window averaging in normal and pathological motor unit action potential trains. Issue 6 (June 2018)

• Record Type: Journal Article
• Authors: Malanda-Trigueros, Armando; Navallas, Javier; Rodriguez-Falces, Javier; Rodriguez-Carreño, Ignacio; Porta, Sonia; Fernández-Martínez, Miguel; Gila, Luis
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 6(2018:Jun.)
• Page Start: 1170
• Page End: 1181
• Publication Date: 2018-06
• Subjects: BPM best performing method -- EA ensemble averaging -- EMG electromyography -- FCA five-closest averaging -- GSMW gold standard MUAP waveforms -- MA median averaging -- MUAP motor unit action potential -- MWP MUAP waveform parameters -- NBP normalized baseline power -- NDEP normalized differential error power -- NEP normalized error power -- SD standard deviation -- SPMF signal processing merit figures -- SWSA sliding window selective averaging; Electromyography -- Motor unit action potential -- Averaging -- Sliding-window -- MUAP waveform; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2018.02.134
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: A new sliding window algorithm for averaging trains of MUAPs has been tested. It performed better than relevant averaging algorithms with normal, myopathic and neurogenic signals. The algorithm can be of service for the quantitative analysis of MUAP waveforms. 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.