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Record W1966159268 · doi:10.1109/embc.2012.6346655

Blood pressure estimation using maximum slope of oscillometric pulses

2012· article· en· W1966159268 on OpenAlexaff
M. Mafi, Sreeraman Rajan, Miodrag Bolić, V.Z. Groza, Hilmi R. Dajani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBlood pressureWaveformEnvelope (radar)Pulse pressurePulse (music)MathematicsAmplitudePulse-amplitude modulationBiomedical engineeringMaterials scienceMedicinePhysicsComputer scienceInternal medicineOpticsTelecommunications

Abstract

fetched live from OpenAlex

A new oscillometric pulse index (OPI) derived from the maximum slope (MS) of each pulse in the oscillometric blood pressure waveform is proposed for blood pressure estimation. Maximum slope for each pulse is obtained using the first derivative of the pulse and an envelope of the values corresponding to the maximum slopes is obtained. The maximum of the envelope is taken as the mean arterial pressure (MAP) and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates are obtained as a fraction of the MAP, similar to the traditional maximum amplitude algorithm (MAA). The proposed algorithm is tested on 18 healthy subjects. The MAP, SBP and DBP estimates obtained from the proposed algorithm are compared with those obtained from a commercial blood pressure device and with the estimates obtained using the MAA and morphological qualitative measures available in the literature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2012
Admission routes1
Has abstractyes

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