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Record W4388415121 · doi:10.1002/ima.22991

Non‐invasive measurement of blood pressure waveform based on spatiotemporal multi‐dimensional pulse waves

2023· article· en· W4388415121 on OpenAlexaff
Dongmei Lin, Hengkun Wang, Aihua Zhang, Jason Gu, Xiaolei Chen, Ce Li, Yurun Ma

Bibliographic record

VenueInternational Journal of Imaging Systems and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsWaveformPulse wavePulse (music)Pulse Wave AnalysisAcousticsBlood pressureComputer scienceMeasure (data warehouse)Pulse pressureArtificial intelligenceOpticsBiomedical engineeringPhysicsMedicineTelecommunicationsInternal medicineLaserData mining

Abstract

fetched live from OpenAlex

Abstract Continuous monitoring of blood pressure (BP) waveform is essential for the prevention and diagnosis of cardiovascular diseases because it contains rich information. A vision‐based non‐invasive measurement method of BP waveform was proposed in this work. A binocular vision pulse acquisition system was utilized to collect continuous pulse images in a predetermined area of radial artery, and multi‐dimensional pulse waves were extracted from image sequences. By using Bi‐directional Long‐Short Term Memory (BiLSTM) networks, a model suitable for the inputs of multitype pulse waves under multi‐length windows has been established. The model can measure BP wave with high accuracy more than 0.94 (correlation coefficient). Measurement errors are less than (4.65 ± 5.80) mmHg for systolic blood pressure and (6.45 ± 7.40) mmHg for diastolic blood pressure. This study breaks through the limitation of obtaining BP from single‐dimensional pulse wave in previous studies and provides a new idea for non‐invasive measurement of human BP waveform.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.109
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.229
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

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