Paper Info
Title
A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings.
Abstract
Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical Abstract The raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.
Year
DOI
Venue
2018
10.1007/s11517-017-1773-0
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Electromyography,Scanning-EMG,Signal processing,Motor unit
Least squares,Signal processing,Computer vision,Median filter,Pattern recognition,Waveform,Smoothing,Motor unit,Interference (wave propagation),Artificial intelligence,Signal processing algorithms,Mathematics
Journal
Volume
Issue
ISSN
56
8
0140-0118
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Íñigo Corera120.77
Adrián Eciolaza200.34
Oliver Rubio300.34
Armando Malanda4144.06
Javier Rodriguez-Falces5166.53
Javier Navallas6185.33