EarSteth: Cardiac Auscultation Audio Reconstruction Using Earbuds
Bibliographic record
Abstract
Cardiac auscultation is often impractical in telehealth settings because it requires that physicians be co-located with patients in order to operate a stethoscope. We address this gap with EarSteth - a system that leverages consumer-grade active noise-cancelling earbuds to reconstruct cardiac auscultation audio signals. The system processes audio captured by the earbuds' inner microphone with a machine learning model that reconstructs audio similar to what would be produced by a digital stethoscope during cardiac auscultation. We evaluate two existing audio super-resolution CNNs and further adapt them for heart sound reconstruction, resulting in a proposed model called EarStethNet. EarSteth models were trained using synchronous audio collected from 15 healthy adult participants with an earbud and a digital stethoscope. We found that EarStethNet was able to estimate interbeat interval with a mean absolute error of 36.6 ± 51.1 ms and was able to reconstruct cardiac auscultation audio with a mean log spectral distance of 1.22 dB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".