Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography.

Mohammadi H, Tarvirdizadeh B, Alipour K, Ghamari M

DOI: 10.1177/14604582251406449
Published: 2025 Oct-Dec
Container: Health informatics journal
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1177/14604582251406449,
  title = {Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography.},
  author = {Mohammadi H and Tarvirdizadeh B and Alipour K and Ghamari M},
  year = {2025},
  journal = {Health informatics journal},
  doi = {10.1177/14604582251406449},
  url = {https://doi.org/10.1177/14604582251406449}
}

RIS

TY  - JOUR
TI  - Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography.
AU  - Mohammadi H
AU  - Tarvirdizadeh B
AU  - Alipour K
AU  - Ghamari M
PY  - 2025
JO  - Health informatics journal
DO  - 10.1177/14604582251406449
UR  - https://doi.org/10.1177/14604582251406449
ER  -

APA

H, M., B, T., K, A., & M, G. (2025). Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with photoplethysmography.. Health informatics journal. https://doi.org/10.1177/14604582251406449

Source records

pubmed · retrieved 2026-09-25T08:18:23.988Z