The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets
Open accessAbstract
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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Cites
Determining Lyapunov exponents from a time series
Wolf, A., Swift, J. B., Swinney, H. L., & Vastano, J. A. (1985) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(85)90011-9 · Cited by 9,499
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880
Approximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811
Multiscale entropy analysis of biological signals
Costa, M., Goldberger, A. L., & Peng, C.-K. (2005) · Physical Review E · doi:10.1103/physreve.71.021906 · Cited by 2,482
Bar-yam, Y. (2019) · CRC Press eBooks · doi:10.1201/9780429034961 · Cited by 1,375
Loss of 'Complexity' and Aging: Potential Applications of Fractals and Chaos Theory to Senescence
Lipsitz, L. A. (1992) · JAMA · doi:10.1001/jama.1992.03480130122036 · Cited by 1,256
Sample entropy analysis of neonatal heart rate variability
Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00069.2002 · Cited by 1,233
Approximate entropy (ApEn) as a complexity measure
Pincus, S. (1995) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166092 · Cited by 1,213
The Quark and the Jaguar: Adventures in the Simple and the Complex
Gell-Mann, M., & Mermin, D. (1994) · Physics Today · doi:10.1063/1.2808634 · Cited by 926
The quark and the jaguar: adventures in the simple and the complex
(1994) · Choice Reviews Online · doi:10.5860/choice.32-0997 · Cited by 786
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Alzheimer's disease and automatic speech analysis: A review
Pulido, M. L. B., Hernández, J. B. A., Ballester, M. Á. F., González, C. M. T., Mekyska, J., & Smékal, Z. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113213 · Cited by 120
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Metadata
- Authors
- Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N.
- Venue
- Annals of Biomedical Engineering
- Published
- 2012-10-12
- Volume
- 41
- Issue
- 2
- Pages
- 349-365
- ISSN
- 0090-6964 (ISSN-L)
- 1573-9686
- OpenAlex
- W1964000773
- Cited by
- 893
- References
- 42
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N. (2013). The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets. Annals of Biomedical Engineering. https://doi.org/10.1007/s10439-012-0668-3
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