Enhanced Phonocardiogram Classification Performance through Outlier Detection
Bibliographic record
Abstract
Deep learning has been utilized in binary classi-fication of phonocardiogram (PCG) signals. Current learning techniques, segment PCG records into cycles for training. As the cycles are unlabelled, the cycle labels are inherited from the record labels, leading to wrong labelling of some of the abnormal cycles. Moreover, the heterogeneity within the training dataset arising from the use of different sensors, location of data acquisition, and different pathology further negatively impact the learning and subsequently the binary classification of the record as either normal or abnormal. Therefore, this work proposes a novel individual record-based method, instead of the whole dataset-based method to mitigate mislabelling of abnormal cycles during the training process and to simultaneously addressing heterogeneity in the dataset. Specifically, each abnormal PCG record undergoes individual outlier detection (due to hetero-geneity), enabling the detection and removal of outlier cycles before the PCG record is utilized for training. Thus, training is conducted using properly labelled abnormal and normal cycles. Comprehensive evaluations comparing with baseline methods across various data subsets is carried out. A range of performance metrics is considered to evaluate the proposed method. Results emphasize the effectiveness of integrating outlier detection into the proposed methodology, thereby significantly enhancing the accuracy of PCG classification.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".