Cited by 11Non-Invasive Vital Sign MonitoringArtificial Intelligence in HealthcareCardiovascular Health and Disease Prevention

Prediction of Ankle Brachial Index with Photoplethysmography Using Convolutional Long Short Term Memory

Lee, Jeong Jik, Heo, Jeong Hyun, Han, Ji Ho, Kim, Bo Ram, Gwon, Hyeok Yong, Yoon, Young Ro·2020·Journal of Medical and Biological Engineering

Integrity signals

Registry

Crossref · 10.1007/s40846-020-00507-w

Cites

46 of 46 references verified0 citation errors · 0 not checkable

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  • Deep learning has recently been used to develop a model for assessing arterial stiffness from the PPG.

    “deep learning has recently been used to develop a model for assessing arterial stiffness from the PPG”

    From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [173]

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Authors
Lee, J. J., Heo, J. H., Han, J. H., Kim, B. R., Gwon, H. Y., & Yoon, Y. R.
Affiliations
Yonsei University (KR)
Corresponding author
Yoon, Young Ro
Venue
Journal of Medical and Biological Engineering
Published
2020-02-18
Volume
40
Issue
2
Pages
282-291
ISSN
  • 1609-0985 (ISSN-L)
  • 2199-4757
OpenAlex
W3008239069
Cited by
11
References
46
Citations by year
2025: 1 · 2024: 1 · 2022: 5 · 2021: 4
FWCI
0.71
Type
journal article
Language
en
Open access
closed
Locations
Journal of Medical and Biological Engineering (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
Keywords
ankle-brachial index, photoplethysmography, convolutional long short-term memory, cardiovascular disease detection, arterial disease, non-invasive blood pressure measurement, deep learning classification, primary care

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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