Template-Based Extraction of Interbeat Intervals from in-Ear Heartbeat Sounds
Bibliographic record
Abstract
Recent advancements in hearable technology have led to the development of multifunctional in-ear devices equipped with microphones that can improve audio experiences, assist with hearing and enhance speech communication. This paper introduces a template-based algorithm for extracting interbeat intervals (IBIs) from heartbeat sounds captured by acoustic in-ear microphones. The proposed method involves generating a template using the median waveform of the heartbeat sound, using this template for correlation-based peak detection, and detecting anomalies in the resulting peaks. The algorithm is evaluated on 39 participants in silence and noise, with ground truth provided by concurrent electrocardiography measurements. Results demonstrate that the template-based approach improves the accuracy of IBI extraction, achieving 4.68 milliseconds mean absolute error using a 300-second analysis window, which is an improvement of 55.13% from the benchmark method found in the literature. This study highlights the potential of template-based algorithms for heart rate variability analysis using acoustic in-ear microphones, broadening the applications of hearable technology for continuous non-invasive health 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".