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

Jennifer M. Yentes, Nathaniel Hunt, Kendra K. Schmid, Jeffrey P. Kaipust, Denise McGrath, Nicholas Stergiou

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    398 Citations (Scopus)

    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.
    Original languageEnglish
    JournalAnnals of Biomedical Engineering
    VolumeOnline
    DOIs
    Publication statusPublished - Oct 2012

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    Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N. (2012). The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets. Annals of Biomedical Engineering, Online. https://doi.org/10.1007/s10439-012-0668-3