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Record W2535372083 · doi:10.1109/embc.2016.7591820

An accelerometer based system to measure myocardial performance index during stress testing

2016· article· en· W2535372083 on OpenAlexaff
Anh Dinh, Francis M. Bui, Tam Huu Nguyen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAccelerometerMetric (unit)Measure (data warehouse)Stress testing (software)Cardiac cycleIndex (typography)CardiologyHeart rateInternal medicineComputer scienceMedicineEngineeringBlood pressureData mining

Abstract

fetched live from OpenAlex

Stress testing is used to measure the performance of the heart in an elevated stress state, in order to monitor or diagnose certain heart problems. Many measurements can be used to determine the performance of the heart, with the Tei index being the measurement of interest in this work. The Tei index has been used as a reliable method to evaluate systolic and diastolic performance, as it overcomes some limitations of the classical echocardiographic indices. It is calculated based on the time intervals derived from echocardiography. This paper presents an exploratory study, which uses an accelerometer to record mechanical events occurring in each cardiac cycle, also known as the seismocardiogram (SCG). From timing measurements corresponding to various events in the heart, a metric for myocardial performance is calculated based on the Tei index. The use of SCG in addition to ECG has the potential to provide further insights about the heart during stress testing, since the SCG quantifies mechanical actions of the heart.

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 score0.101
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.204
Teacher spread0.185 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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