Abstract 11669: “Hearing the Heart” - Validation of a Novel Digital Health Earbud Technology to Measure Cardiac Time Intervals Through Infrasonic Hemodynography
Bibliographic record
Abstract
Introduction: The cardiovascular (CV) system produces low frequency, ‘infrasonic’, auditory vibrations during the cardiac cycle. Herein, we report the first-in-person validation of a novel earbud sensor to capture CV time intervals and the feasibility of non-invasive infrasonic hemodynography (IH) using the MindMics ® wireless earbuds for long term in-ear CV monitoring. Methods: Infrasonic waveforms were captured during cardiac catheterization (CC) among 5 study subjects wearing the IH ear-buds (Figure A) who underwent CC for the evaluation of coronary artery disease. Simultaneous IH and CC waveforms were acquired and time synchronized at 1000Hz sampling rate as time-series datasets. Each subject underwent echocardiography to identify aortic valve opening/closure (AVO/AVC) and left ventricular (LV) outflow tract flow measurements with hemodynamic waveforms during CC measuring LV ejection time (LVET). Validation of the IH waveform (in-ear acoustic pressure measured in Pascals) was compared to echocardiography (AVO/AVC) and hemodynamic waveforms (LVET) with concordance and Bland-Altman analysis, and with overlaid data visualizations to CV time intervals. Results: 5 study subjects comprised 257 CV cycles with a total data set of >450,000 time-series data points. IH signals collected simultaneously with the pulsed wave Doppler demonstrated alignment with AVO/AVC (Figure B) and were synchronized to CC waveforms in the aorta (Figure C). A high correlation between LVET measured from IH and CC was observed (R=0.87, p<0.0001, Figure D), with a mean absolute error of 14.7ms and a bias of 7.2ms (Figure E) (mean±SEM of 342.3±2.1ms for CC and 349.5±2.1ms for IH). Conclusions: In a first-in-person study, we report high accuracy between IH, echocardiography, and CC hemodynamic waveforms to capture CV time intervals including CV performance measures. Further studies are underway to validate IH and the earbud sensor towards non-invasive hemodynamic monitoring.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".