Approximate entropy based on attempted steady isometric contractions with the ankle dorsal- and plantarflexors: Reliability and optimal sampling frequency

Rose, Martin H.; Bandholm, Thomas; Jensen, Bente Rona

doi:10.1016/j.jneumeth.2008.09.025

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…While previous studies have provided new and important insights into a variety of clinical problems using these nonlinear analyses, it is essential to adopt a prudent approach to the various techniques available for the quantification of entropic properties of a signal. Previous studies have investigated the use of these techniques in various biological signals 4, 33–35 ; however, the application of entropy calculations to gait data requires further exploration. The current study was aimed at the following three goals: 1) to determine the correct input parameters to use, 2) determine which algorithm clearly demonstrates relative consistency and 3) to determine which algorithm provides the best discrimination between groups.…”

Section: Discussionmentioning

confidence: 99%

“…Presently, the two most commonly used methods for biological data are approximate entropy (ApEn) and sample entropy (SampEn). Entropy has been used to describe changes in postural control 8, 33, 40 , physical activity measures 3, 38 , as well as other movement tasks 13, 35, 37 . ApEn and SampEn have also been utilized with human walking data 11, 14–16, 18, 20, 21, 28, 32 .…”

Section: Introductionmentioning

confidence: 99%

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The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

et al. 2012

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Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

et al. 2012

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“…In the last twenty years, the use of entropy methods to define periodicity or regularity in human data is increasingly popular [44]. Entropy has been used to describe changes in human physiological signals like postural control [45] as well as movement tasks [46]. Information entropy by Shannon is a foundational concept of information theory which quantifies the amount of information in a variable [47].…”

Section: Phase 3: Calculating Rcg Time Entropymentioning

confidence: 99%

Radar Command Group Time Entropy Signature as a Visual Monitoring Enhancement for Air Traffic Controllers

Wee

Lye

Pinheiro

et al. 2021

IEEE Trans. Human-Mach. Syst.

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“…Updated samples were digitally band-pass filtered (Butterworth, 2th order, 10-400 Hz) and a notch filter (2nd order Butterworth band stop with rejection width 1 Hz centered at power line frequency (50 Hz)) was applied. RMS and permuted sample entropy (PeSaEn) were calculated over 500 ms segments decimated by factor of two since a redundant sampling frequency leads to underestimation of PeSaEn [31] and increases the computational cost. Figure 1b shows the schematic of online processing procedure.…”

Section: Feedback Decision Machine (Online Processing)mentioning

confidence: 99%

Advanced biofeedback from surface electromyography signals using fuzzy system

Samani

Holtermann

Søgaard

et al. 2010

Med Biol Eng Comput

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The aims of this study were to develop a fuzzy inference-based biofeedback system and investigate its effects when inducing active (shoulder elevation) and passive (relax) pauses on the trapezius muscle electromyographic (EMG) activity during computer work. Surface EMG signals were recorded from clavicular, descending (bilateral) and ascending parts of the trapezius muscles during computer work. The fuzzy system readjusted itself based on the history of previous inputs. The effect of feedback was assessed in terms of muscle activation regularity and amplitude. Active pause resulted in non-uniform muscle activity changes in the trapezius muscle depicted by increase and decrease of permuted sample entropy in ascending and clavicular parts of trapezius, respectively (P < 0.05) compared with no pause. Concomitantly, the normalized root mean square of EMG increased approximately 5% in descending part of trapezius bilaterally (P < 0.01). These findings confirm that advanced feedback can change the pattern of muscle activation.

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Approximate entropy based on attempted steady isometric contractions with the ankle dorsal- and plantarflexors: Reliability and optimal sampling frequency

Cited by 10 publications

References 33 publications

The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets

Radar Command Group Time Entropy Signature as a Visual Monitoring Enhancement for Air Traffic Controllers

Advanced biofeedback from surface electromyography signals using fuzzy system

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