ECG sonification methods for robust and generalizable clinical decision support
Bibliographic record
Abstract
Electrocardiography (ECG) sonification has emerged as a complementary modality to visual waveform interpretation, enabling diagnostic cues to be conveyed through sound in real time. This review synthesizes evidence from the past decade to establish the current methodological landscape and gaps in ECG sonification research. A structured search identified eight peer-reviewed studies (2015-2025) that applied auditory transformation of ECG signals for diagnosis, monitoring, or therapeutic guidance. We analyzed signal acquisition methods, preprocessing pipelines, mapping strategies (including parameter mapping and amplitude/frequency modulation, with optional machine-learning decoding), evaluation endpoints, and common limitations. Reported benefits included enhanced perceptual cue detection, alternative feedback channels, and support for cognitive load reduction. However, generalizability was limited by small sample sizes, inconsistent reporting of audio design parameters, and minimal clinical validation. We propose minimum reporting standards and a staged evaluation pathway to enable robust, reproducible, and clinically translatable ECG sonification systems.
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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 | 0.119 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".