Evidence map›Paper›PMID 37324377›Full record

ArticleFrontiers in physiology2023

Calculating sample entropy from isometric torque signals: methodological considerations and recommendations.

Peter C Raffalt, Jennifer M Yentes, Sandro R Freitas, João R Vaz

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Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Peter C RaffaltDepartment of Biology, University of Southern Denmark, Odense, Denmark.
Jennifer M YentesDepartment of Kinesiology & Sport Management, Texas A&M University, College Station, TX, United States.
Sandro R FreitasFaculdade de Motricidade Humana, Universidade de Lisboa, Lisboa, Portugal.
João R VazEgas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, Caparica, Almada, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

motor controlmuscle contractionnonlinear analysisregularitytime series

Identifiers

PMID37324377
PMCPMC10267410

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.