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Record W2589940202 · doi:10.1117/12.2252114

Assessing photoplethysmographic imaging performance beyond facial perfusion analysis

2017· article· en· W2589940202 on OpenAlexaff
Robert Amelard, Richard L. Hughson, Danielle K. Greaves, David A. Clausi, Alexander Wong

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
Fundersnot available
KeywordsMedicineBiomedical engineeringArteryNuclear medicineCardiology

Abstract

fetched live from OpenAlex

Photoplethysmographic imaging (PPGI) systems are relatively new non-contact biophotonic diffuse reflectance systems able to assess arterial pulsations through transient changes in light-tissue interaction. Many PPGI studies have focused on extracting heart rate from the face or hand. Though PPGI systems can be used for widefield imaging of any anatomical area, whole-body investigations are lacking. Here, using a novel PPGI system, coded hemodynamic imaging (CHI), we explored and analyzed the pulsatility at major arterial locations across the whole body, including the neck (carotid artery), arm/wrist (brachial, radial and ulnar arteries), and leg/feet (popliteal and tibial arteries). CHI was positioned 1.5 m from the participant, and diffuse reactance from a broadband tungsten-halogen illumination was filtered using 850{1000 nm bandpass filter for deep tissue penetration. Images were acquired over a highly varying 24-participant sample (11/13 female/male, age 28.7&plusmn;12.4 years, BMI 25.5&plusmn;5.2 kg/m<sup>2</sup>), and a preliminary case study was performed. B-mode ultrasound images were acquired to validate observations through planar arterial characteristics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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