Improving the performance of sample entropy in ultra-short-term time-series using kernel density estimation

Chang Yan, Kaiyue Si, Zhaoyang Cong, Annabella Sihan Dai, Chengyu Liu, Peng Li

Open source

DOI: 10.1088/1361-6579/aea5d3
Published: 2026-09-24
Container: Physiological Measurement
Publisher: IOP Publishing
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1088/1361-6579/aea5d3,
  title = {Improving the performance of sample entropy in ultra-short-term time-series using kernel density estimation},
  author = {Chang Yan and Kaiyue Si and Zhaoyang Cong and Annabella Sihan Dai and Chengyu Liu and Peng Li},
  year = {2026},
  journal = {Physiological Measurement},
  doi = {10.1088/1361-6579/aea5d3},
  url = {https://doi.org/10.1088/1361-6579/aea5d3}
}

RIS

TY  - JOUR
TI  - Improving the performance of sample entropy in ultra-short-term time-series using kernel density estimation
AU  - Chang Yan
AU  - Kaiyue Si
AU  - Zhaoyang Cong
AU  - Annabella Sihan Dai
AU  - Chengyu Liu
AU  - Peng Li
PY  - 2026
JO  - Physiological Measurement
DO  - 10.1088/1361-6579/aea5d3
UR  - https://doi.org/10.1088/1361-6579/aea5d3
ER  -

APA

Yan, C., Si, K., Cong, Z., Dai, A. S., Liu, C., & Li, P. (2026). Improving the performance of sample entropy in ultra-short-term time-series using kernel density estimation. Physiological Measurement. https://doi.org/10.1088/1361-6579/aea5d3

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crossref · retrieved 2026-09-26T01:19:47.336Z