A Fusion of Handcrafted Features and Deep Learning Classifiers for Heart Murmur Detection
Why this work is in the frame
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Bibliographic record
Abstract
As part of George B. Moody Physionet Challenge 2022, our team Melbourne Kangas, proposed an algorithm for identifying abnormal heart sounds from paediatric phonocardiograms (PCGs).We developed a Deep Learning (DL) approach and a handcrafted feature-based approach.The DL classifier was based on bidirectional long-short-termmemory and Mel-frequency cepstrum coefficients from raw PCG signals.The feature-based approach used nonnegative matrix factorisation to denoise PCG signals and then extracted the features based on the whole and segmented recordings, followed by feature selection.A random under-sampling boosting classifier for murmur classification and robust boosting classifier for outcome classification were given the subset of features.The feature-based performed better than the DL classifiers on the validation set.The feature-based classifier received a weighted accuracy of 0.632 (29th out of 41 teams) and a challenge cost of 11,735 (3rd out of 39 teams) on the test set.Decision fusion of the two approaches decreased 10-fold crossvalidation results.
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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 it