Phonocardiogram Classification by Learning From Positive and Unlabeled Examples
Bibliographic record
Abstract
The advent of deep learning has rekindled research in computer-aided auscultation for the classification of phonocardiogram (PCG) signals. Deep learning techniques require a large labeled dataset for training. However, labeling large datasets is a formidable task. In order to create the large labeled dataset, the labeled PCG signal records are segmented cycle-wise, and the labels of the records are passed on to all the cycles of the record. Although this label inheritance may be appropriate for segments of the normal PCG signals, it may be inappropriate for abnormal PCGs and therefore may result in the wrong labeling of cycles of abnormal PCG signals. To address this issue, we propose positive unlabeled (PU) learning based on a two-step technique deep learning model for the classification of PCG signals where PCG segments of normal records are considered positive exemplars and PCG segments of abnormal records are considered unlabeled. To attain the final results, a voting method is employed using a differential evolution algorithm (DE) and majority voting. The system was evaluated using a dataset from the 2016 Physionet/Computing in Cardiology Challenge. The proposed system achieved exceptional record classification performance with a score of about 0.95. Regardless of an imbalanced dataset, the method achieved balanced specificity and sensitivity values of about 0.94 and 0.95, respectively. Additionally, the proposed system outperformed existing human PCG binary classification systems. In conclusion, utilizing PU learning and CNN techniques for diagnosing heart sounds can lead to effective and efficient classification.
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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 | 0.000 |
| 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) | 0.000 | 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".