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Stroke-Related Changes in the Complexity of Muscle Activation During Obstacle Crossing Using Fuzzy Approximate Entropy Analysis

Overview
Journal Front Neurol
Specialty Neurology
Date 2018 Mar 30
PMID 29593632
Citations 3
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Abstract

This study investigated the complexity of the electromyography (EMG) of lower limb muscles when performing obstacle crossing tasks at different heights in poststroke subjects versus healthy controls. Five poststroke subjects and eight healthy controls were recruited to perform different obstacle crossing tasks at various heights (randomly set at 10, 20, and 30% of the leg's length). EMG signals were recorded from bilateral biceps femoris (BF), rectus femoris (RF), medial gastrocnemius, and tibialis anterior during obstacle crossing task. The fuzzy approximate entropy (fApEn) approach was used to analyze the complexity of the EMG signals. The fApEn values were significantly smaller in the RF of the trailing limb during the swing phase in poststroke subjects than healthy controls ( < 0.05), which may be an indication of smaller number and less frequent firing rates of the motor units. However, during the swing phase, there were non-significant increases in the fApEn values of BF and RF in the trailing limb of the stroke group compared with those of healthy controls, resulting in a coping strategy when facing challenging tasks. The fApEn values that increased with height were found in the BF of the leading limb during the stance phase and in the RF of the trailing limb during the swing phase ( < 0.05). The reason for this may have been a larger muscle activation associated with the increase in obstacle height. This study demonstrated a suitable and non-invasive method to evaluate muscle function after a stroke.

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References
1.
Pincus S . Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A. 1991; 88(6):2297-301. PMC: 51218. DOI: 10.1073/pnas.88.6.2297. View

2.
Li X, Wang Y, Suresh N, Rymer W, Zhou P . Motor unit number reductions in paretic muscles of stroke survivors. IEEE Trans Inf Technol Biomed. 2011; 15(4):505-12. DOI: 10.1109/TITB.2011.2140379. View

3.
Chen Y, Yang Z . A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity. Front Neurosci. 2017; 11:61. PMC: 5307491. DOI: 10.3389/fnins.2017.00061. View

4.
Pincus S . Approximate entropy (ApEn) as a complexity measure. Chaos. 1995; 5(1):110-117. DOI: 10.1063/1.166092. View

5.
Gitter J, Czerniecki M . Fractal analysis of the electromyographic interference pattern. J Neurosci Methods. 1995; 58(1-2):103-8. DOI: 10.1016/0165-0270(94)00164-c. View