Phonocardiogram Classification Using Dynamic Mode Decomposition for Heterogeneity-Resilient Training
Bibliographic record
Abstract
Medical measurements of the heart, such as a phonocardiogram (PCG), provide a non-invasive means for diagnosing valvular heart diseases. Recently, deep learning has shown great success in the classification of PCG. Current deep learning models for binary classification of PCG utilize measurements obtained in controlled or uncontrolled environments using different stethoscope sensors, each with distinct characteristics, placed at different measurement sites on the heart. Such acquisitions introduce heterogeneity in the measurements that are used for training deep learning PCG classification models. Such trained models deliver reduced balanced accuracy. Training a separate deep learning model for each sensor or each measurement site is impractical. Therefore, a unified model that can generalize across sensor and measurement variations is needed. In this work, Dynamic Mode Decomposition (DMD) in conjunction with domain-balanced training is proposed to address measurement heterogeneity and enable robust model training on mixed-source measurements. Experimental results show that the proposed heterogeneity addressing method outperforms existing approaches that ignore heterogeneity by achieving approximately 15% improvement in balanced accuracy, while 4% improvement in balanced accuracy amongst existing methods that address heterogeneity in binary classification of PCG.
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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) | 0.001 | 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".