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Calculating sample entropy from isometric torque signals: methodological considerations and recommendations
We investigated the effect of different sampling frequencies, input parameters and observation times for sample entropy (SaEn) calculated on torque data recorded from a submaximal isometric contraction. Forty-six participants performed sustained isometric knee flexion at 20% of their maximal contrac...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267410/ https://www.ncbi.nlm.nih.gov/pubmed/37324377 http://dx.doi.org/10.3389/fphys.2023.1173702 |
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author | Raffalt, Peter C. Yentes, Jennifer M. Freitas, Sandro R. Vaz, João R. |
author_facet | Raffalt, Peter C. Yentes, Jennifer M. Freitas, Sandro R. Vaz, João R. |
author_sort | Raffalt, Peter C. |
collection | PubMed |
description | We investigated the effect of different sampling frequencies, input parameters and observation times for sample entropy (SaEn) calculated on torque data recorded from a submaximal isometric contraction. Forty-six participants performed sustained isometric knee flexion at 20% of their maximal contraction level and torque data was sampled at 1,000 Hz for 180 s. Power spectral analysis was used to determine the appropriate sampling frequency. The time series were downsampled to 750, 500, 250, 100, 50, and 25 Hz to investigate the effect of different sampling frequency. Relative parameter consistency was investigated using combinations of vector lengths of two and three and tolerance limits of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, and 0.4, and data lengths between 500 and 18,000 data points. The effect of different observations times was evaluated using Bland-Altman plot for observations times between 5 and 90 s. SaEn increased at sampling frequencies below 100 Hz and was unaltered above 250 Hz. In agreement with the power spectral analysis, this advocates for a sampling frequency between 100 and 250 Hz. Relative consistency was observed across the tested parameters and at least 30 s of observation time was required for a valid calculation of SaEn from torque data. |
format | Online Article Text |
id | pubmed-10267410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102674102023-06-15 Calculating sample entropy from isometric torque signals: methodological considerations and recommendations Raffalt, Peter C. Yentes, Jennifer M. Freitas, Sandro R. Vaz, João R. Front Physiol Physiology We investigated the effect of different sampling frequencies, input parameters and observation times for sample entropy (SaEn) calculated on torque data recorded from a submaximal isometric contraction. Forty-six participants performed sustained isometric knee flexion at 20% of their maximal contraction level and torque data was sampled at 1,000 Hz for 180 s. Power spectral analysis was used to determine the appropriate sampling frequency. The time series were downsampled to 750, 500, 250, 100, 50, and 25 Hz to investigate the effect of different sampling frequency. Relative parameter consistency was investigated using combinations of vector lengths of two and three and tolerance limits of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, and 0.4, and data lengths between 500 and 18,000 data points. The effect of different observations times was evaluated using Bland-Altman plot for observations times between 5 and 90 s. SaEn increased at sampling frequencies below 100 Hz and was unaltered above 250 Hz. In agreement with the power spectral analysis, this advocates for a sampling frequency between 100 and 250 Hz. Relative consistency was observed across the tested parameters and at least 30 s of observation time was required for a valid calculation of SaEn from torque data. Frontiers Media S.A. 2023-06-01 /pmc/articles/PMC10267410/ /pubmed/37324377 http://dx.doi.org/10.3389/fphys.2023.1173702 Text en Copyright © 2023 Raffalt, Yentes, Freitas and Vaz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Raffalt, Peter C. Yentes, Jennifer M. Freitas, Sandro R. Vaz, João R. Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title | Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title_full | Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title_fullStr | Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title_full_unstemmed | Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title_short | Calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
title_sort | calculating sample entropy from isometric torque signals: methodological considerations and recommendations |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267410/ https://www.ncbi.nlm.nih.gov/pubmed/37324377 http://dx.doi.org/10.3389/fphys.2023.1173702 |
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