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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Yentes, Jennifer M., Hunt, Nathaniel, Schmid, Kendra K., Kaipust, Jeffrey P., McGrath, Denise and Stergiou, Nicholas (2012) The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets. Annals of Biomedical Engineering, Online first . [Journal article]

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DOI: 10.1007/s10439-012-0668-3

Abstract

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, espe-cially 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 datasets,N£200. We suggest usingNlarger than 200, an mof 2 and examine severalrvalues before selecting your parame-ters. 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.

Item Type:Journal article
Faculties and Schools:Faculty of Life and Health Sciences
Faculty of Life and Health Sciences > Ulster Sports Academy
Research Institutes and Groups:Sport and Exercise Sciences Research Institute
ID Code:23632
Deposited By:Dr Denise McGrath
Deposited On:12 Nov 2012 16:30
Last Modified:12 Nov 2012 16:30

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