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Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury
BACKGROUND: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850704/ https://www.ncbi.nlm.nih.gov/pubmed/27129309 http://dx.doi.org/10.1186/s12984-016-0151-8 |
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author | Jordanic, Mislav Rojas-Martínez, Mónica Mañanas, Miguel Angel Alonso, Joan Francesc |
author_facet | Jordanic, Mislav Rojas-Martínez, Mónica Mañanas, Miguel Angel Alonso, Joan Francesc |
author_sort | Jordanic, Mislav |
collection | PubMed |
description | BACKGROUND: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention. METHOD: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. RESULTS: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard). Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05). CONCLUSION: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences. |
format | Online Article Text |
id | pubmed-4850704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48507042016-04-30 Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury Jordanic, Mislav Rojas-Martínez, Mónica Mañanas, Miguel Angel Alonso, Joan Francesc J Neuroeng Rehabil Research BACKGROUND: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention. METHOD: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. RESULTS: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard). Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05). CONCLUSION: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences. BioMed Central 2016-04-29 /pmc/articles/PMC4850704/ /pubmed/27129309 http://dx.doi.org/10.1186/s12984-016-0151-8 Text en © Jordanic et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Jordanic, Mislav Rojas-Martínez, Mónica Mañanas, Miguel Angel Alonso, Joan Francesc Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title | Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title_full | Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title_fullStr | Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title_full_unstemmed | Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title_short | Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury |
title_sort | spatial distribution of hd-emg improves identification of task and force in patients with incomplete spinal cord injury |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850704/ https://www.ncbi.nlm.nih.gov/pubmed/27129309 http://dx.doi.org/10.1186/s12984-016-0151-8 |
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