Heart sound analysis using the S transform
Bibliographic record
Abstract
Central to this study was a pathological sound called opening snap (OS). The first objective was to detect this sound in recordings in which it occurred too close in rime to one of the normal sounds, the second heart sound (S2). The second objective was to differentiate between the OS and another sound, the third heart sound (S3), the timing of which is similar to that of OS. Both OS and S3 occur shortly after S2. Three techniques were used and their performance was compared: The Short Time Fourier Transform (STFT), the S Transform (ST) and the Continuous Wavelet Transform (CWT). These transforms yield time-frequency or time-scale representations of the signal. In addition, the ST and the CWT are multiresolution transforms. The ST proved to be the best in meeting the objectives, mainly because it yielded highly distinct patterns for each kind of sound. Being a relatively new transform, the ST had not been applied to heart sounds in the past.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".