Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals
- DOI
- 10.1007/s13246-026-01782-8
- Published
- 2026-08-06
- Container
- Physical and Engineering Sciences in Medicine
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1007/s13246-026-01782-8,
title = {Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals},
author = {Hanieh Mohammadi and Bahram Tarvirdizadeh and Khalil Alipour and Mohammad Ghamari},
year = {2026},
journal = {Physical and Engineering Sciences in Medicine},
doi = {10.1007/s13246-026-01782-8},
url = {https://doi.org/10.1007/s13246-026-01782-8}
}RIS
TY - JOUR TI - Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals AU - Hanieh Mohammadi AU - Bahram Tarvirdizadeh AU - Khalil Alipour AU - Mohammad Ghamari PY - 2026 JO - Physical and Engineering Sciences in Medicine DO - 10.1007/s13246-026-01782-8 UR - https://doi.org/10.1007/s13246-026-01782-8 ER -
APA
Mohammadi, H., Tarvirdizadeh, B., Alipour, K., & Ghamari, M. (2026). Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals. Physical and Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-026-01782-8
Source records
- crossref · retrieved 2026-09-25T20:56:47.356Z