A Deep Convolutional Neural Network Classification of Heart Sounds using Fractional Fourier Transform
Bibliographic record
Abstract
A computer-aided auscultation system can help in the initial diagnosis of heart diseases. In this work, we propose a binary classification system that uses fractional Fourier transform based Mel-frequency spectral coefficients (FrFT-MFSC) and a 1D deep convolutional neural network. FrFt-MFSC is used to convert the phonocardiogram (PCG) into heat maps using four fractional orders (0.9, 0.95, 1.0, 1.10). We verify the performance of our proposed system using a publicly available data set that was provided by 2016 Physionet/Computing in Cardiology Challenge. Ten-fold cross-validation and holdout test methods are used to evaluate the performance of the system. Classifier performance for various features using different fractional orders is also studied. The 10-fold cross-validation provides a good performance and balanced specificity and sensitivity of 0.97 and 0.95 respectively despite using imbalance data set. The proposed system performance is superior to all the current state-of-the art binary human PCG classification systems.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".