The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automatic Selection of the Threshold Value $r$ for Approximate Entropy
Abstract
Calculation of approximate entropy (ApEn) requires a priori determination of two unknown parameters, m and r. While the recommended values of r, in the range of 0.1-0.2 times the standard deviation of the signal, have been shown to be applicable for a wide variety of signals, in certain cases, r values within this prescribed range can lead to an incorrect assessment of the complexity of a given signal. To circumvent this limitation, we recently advocated finding the maximum ApEn value by assessing all values of r from 0 to 1, and found that maximum ApEn does not always occur within the prescribed range of r values. Our results indicate that finding the maximum ApEn leads to the correct interpretation of a signal's complexity. One major limitation, however, is that the calculation of all choices of r values is often impractical due to the computational burden. Our new method, based on a heuristic stochastic model, overcomes this computational burden, and leads to the automatic selection of the maximum ApEn value for any given signal. Based on Monte Carlo simulations, we derive general equations that can be used to estimate the maximum ApEn with high accuracy for a given value of m. Application to both synthetic and experimental data confirmed the advantages claimed with the proposed approach.
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Citations
Cites
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 Complex Physiologic Time Series
Costa, M., Goldberger, A. L., & Peng, C.-K. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.068102 · Cited by 3,207
Ho, K. K. L., Moody, G. B., Peng, C.-K., Mietus, J. E., Larson, M. G., Levy, D., & Goldberger, A. L. (1997) · Circulation · doi:10.1161/01.cir.96.3.842 · Cited by 489
Impact of Estradiol Supplementation on Dual Peptidyl Drive of GH Secretion in Postmenopausal Women
Veldhuis, J. D., Evans, W. S., & Bowers, C. Y. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.87.2.8251 · Cited by 36
Cited by
Calkins, H., Kuck, K. H., Cappato, R., Brugada, J., Camm, A. J., Chen, S.-A., Crijns, H. J. G., Damiano, R. J., Davies, D. W., DiMarco, J., et al. (2012) · Europace · doi:10.1093/europace/eus027 · Cited by 2,722
Calkins, H., Kuck, K. H., Cappato, R., Brugada, J., Camm, A. J., Chen, S.-A., Crijns, H. J. G., Damiano, R. J., Davies, D. W., DiMarco, J., et al. (2012) · Heart Rhythm · doi:10.1016/j.hrthm.2011.12.016 · Cited by 1,456
The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets
Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N. (2013) · Annals of Biomedical Engineering · doi:10.1007/s10439-012-0668-3 · Cited by 893
Melillo, P., Bracale, M., & Pecchia, L. (2011) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-10-96 · Cited by 328
Terahertz spectroscopic material identification using approximate entropy and deep neural network
Li, Y., Shen, X. A., Ewing, R. L., & Li, J. (2017) · 2017 IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2017.8268744 · Cited by 14
Metadata
- Authors
- Lu, S., Chen, X., Kanters, J. K., Solomon, I. C., & Chon, K. H.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-08
- Volume
- 55
- Issue
- 8
- Pages
- 1966-1972
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2110815905
- Cited by
- 187
- References
- 4
- FWCI
- 6.82
- Type
- journal article
- Language
- en
- Open access
- closed
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