Heart Sound Classification from Wavelet Decomposed Signal Using Morphological and Statistical Features
Bibliographic record
Abstract
Automatic classification of heart sound recordings is one of the widely known challenges for over 50 years.The fundamental objective of this study is to evaluate a large database of heart sounds collected from a variety of clinical and non-clinical surroundings and classify them into normal and abnormal categories.Daubechis-2 wavelet transform was applied to each phonocardiogram (PCG) recording after segmenting each cardiac cycle into four windows containing first heart sound S1-Systole-Second heart sound (S2)-Diastole states of a heart cycle.Morphological, statistical and time features were extracted from each cardiac states window.Heart sound classification into normal and abnormal was based on the SVM with Gaussian kernel function.The algorithm was trained by the recordings from all available training data sets (training set A to F).The performance of the proposed prototype was evaluated by five-fold cross-validation on the available training dataset as well as on the hidden test set by PhysioNet.Overall classification accuracies of 82% during Phase I submissions and 77% during Phase II submissions were achieved of the challenge.The final score on the blind test set was 74.65%.Based on the current result, the proposed prototype could be a potential solution for a robust and automatic classification technique of normal and abnormal heart sound recordings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".