In vitro and in vivo low frequency acoustic analysis of Bjork-Shiley convexo-concave heart valve opening sounds
Bibliographic record
Abstract
A pattern recognition method using diagnostic features from the low frequency spectra of Bjork-Shiley convexo concave (BSCC) valve opening sounds was tested for differentiating intact valves from those having a single leg separation (SLS) of the outlet strut. The method was applied to valve opening sounds obtained from a pulse duplicator system (6 intact and 6 SLS valves) and recorded in sheep (11 intact and 16 SLS valves). A total of 30 opening sounds were analyzed for each sheep and 10 opening sounds for each pulse duplicator test. For each valve, 21 diagnostic features were extracted from the mean power spectrum of the opening sounds for training and testing the K-nearest neighbor and the Bayes classifiers. All combinations of 1, 2, 3 and 4 features among 21 were evaluated to find the most discriminant feature sets. For the in vitro study, 100% of correct classifications (CCs) was obtained with several feature sets and either classifier. For the sheep study, 93% of CCs was obtained with two feature sets and the Bayes classifier. The feature set that could best classify both in-vivo and in-vitro valve opening sound spectra provided 82% of CCs. The in vitro and in vivo studies thus demonstrated that the low frequency analysis of BSCC opening sounds is a promising method of detecting SLS of BSCC mitral valves.>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".