Time‐Domain Analysis of Heart Sound Intensity in Children with and without Pulmonary Artery Hypertension: A Pilot Study using a Digital Stethoscope
Bibliographic record
Abstract
We studied digital stethoscope recordings in children undergoing simultaneous catheterization of the pulmonary artery (PA) to determine whether time-domain analysis of heart sound intensity would aid in the diagnosis of PA hypertension (PAH). Heart sounds were recorded and stored in .wav mono audio format. We performed recordings for 20 seconds with sampling frequencies of 4,000 Hz at the second left intercostal space and the cardiac apex. We used programs written in the MATLAB 2010b environment to analyze signals. We annotated events representing the first (S1) and second (S2) heart sounds and the aortic (A2) and pulmonary (P2) components of S2. We calculated the intensity (I) of the extracted event area (x) as [Formula: see text], where n is the total number of heart sound samples in the extracted event and k is A2, P2, S1, or S2. We defined PAH as mean PA pressure (mPAp) of at least 25 mmHg with PA wedge pressure of less than 15 mmHg. We studied 22 subjects (median age: 6 years [range: 0.25-19 years], 13 female), 11 with PAH (median mPAp: 55 mmHg [range: 25-97 mmHg]) and 11 without PAH (median mPAp: 15 mmHg [range: 8-24 mmHg]). The P2∶A2 (P = .0001) and P2∶S2 (P = .0001) intensity ratios were significantly different between subjects with and those without PAH. There was a linear correlation (r > 0.7) between the P2∶S2 and P2∶A2 intensity ratios and mPAp. We found that the P2∶A2 and P2∶S2 intensity ratios discriminated between children with and those without PAH. These findings may be useful for developing an acoustic device to diagnose PAH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".