Automatic discrimination between heart sounds and murmurs using parametric models
Bibliographic record
Abstract
Most of the cardiovascular sounds are produced within or about the heart and great vessels and are transmitted through liquid and solid media to the chest wall. During auscultation of the heart, we receive air transmitted sounds through the stethoscope which is limited by the sensitivity of the ear. Present methods for interpretation of heart sounds and murmurs[HSAM]are qualitative and crude depending mainly on experience. Therefore, there is a need for more sensitive and quantitative methods which would permit precise measures for diagnosis. This paper develops a mathematical model to describe the HSAM by a finite number of parameters. The auto regressive model[AR] is selected to represent the HSAM at the principal areas of cardiac auscultation and for different heart diseases. Validation of the model results is based on the minimum mean squared deviation between the signal spectrum obtained by FFT and the estimated spectrum obtained from the AR model. A data base from the simulated HSAM is created and discrimination among the measured HSAM from subjects with different heart diseases is performed. Based on Bayes rule, the discrimination between heart sounds and murmurs is performed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".