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Record W4402721820 · doi:10.1145/3675094.3680526

On the Production and Measurement of Cardiac Sounds in the Ear Canal

2024· article· en· W4402721820 on OpenAlexaff
Kenneth Christofferson, Sejal Bhalla, Joseph A Cafazzo, Alex Mariakakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsEar canalProduction (economics)Computer scienceAcousticsSpeech recognitionPhysics

Abstract

fetched live from OpenAlex

Over the past several years, a growing body of literature has proposed systems that use earable-based acoustic sensing to assess cardiac function. These works have offered various explanations of how in-ear cardiac audio is produced. Most claim that the sounds are caused by compressive waves that travel directly from the chest, while others claim that the sounds are caused by the pulse wave producing arterial expansion near the ear canal. Although these explanations are not mutually exclusive, the lack of consensus raises questions about the working principles and possibilities in this growing research area. We present a series of experiments using a multimodal dataset of cardiac signals to test various hypotheses related to the production of heart sounds in the ear canal. Our results suggest that in-ear cardiac audio contains components produced by both compressive waves and pulse waves.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.029
GPT teacher head0.268
Teacher spread0.240 · 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 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
Published2024
Admission routes1
Has abstractyes

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