Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis

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dc.coverageDOI: 10.3389/fbioe.2022.934041
dc.creatorDe la Fuente, Carlos
dc.creatorWeinstein, Alejandro
dc.creatorNeira, Alejandro
dc.creatorValencia, Oscar
dc.creatorCruz-Montecinos, Carlos
dc.creatorSilvestre, Rony
dc.creatorPincheira, Patricio A.
dc.creatorPalma, Felipe
dc.creatorCarpes, Felipe P.
dc.date2022
dc.date.accessioned2025-11-18T19:42:19Z
dc.date.available2025-11-18T19:42:19Z
dc.description<p>The instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000 ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was &lt;25% and &gt;75%. Entropy decreased when the overlap was &lt;25% and &gt;75% and when the window length was &lt;100 ms and &gt;500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p &lt; 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p &lt; 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.</p>eng
dc.descriptionThe instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000 ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was [removed]75%. Entropy decreased when the overlap was [removed]75% and when the window length was [removed]500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p &lt; 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p &lt; 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.spa
dc.identifierhttps://investigadores.uandes.cl/en/publications/e1f9af3e-277f-4987-a7cf-a41468b35213
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/52285
dc.languageeng
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourcevol.10 (2022) date: 2022-12-22
dc.subjectUMAP
dc.subjectbarycenter
dc.subjectentropy
dc.subjecthigh-density electromyography
dc.subjectimage moments
dc.subjectmuscle
dc.subjectsegmentation
dc.subjectActivation analysis
dc.subjectChemical activation
dc.subjectGeometry
dc.subjectImage analysis
dc.subjectImage segmentation
dc.subjectTopology
dc.titleBiased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysiseng
dc.typeArticleeng
dc.typeArtículospa
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