Develop the Hybrid Empirical Mode Decomposition with Fast Mask CNN to Improve the Performance Measures of PCG Signals
Bibliographic record
Abstract
The Phonocardiogram (PCG) signal provides crucial insights into heart function and is instrumental in identifying cardiac dysfunctions leading to heart failure.Given the significant impact of Cardiovascular Diseases (CVD) on human life and socioeconomic conditions, early detection of cardiac problems is imperative.This study evaluates a hybrid denoising technique, Empirical Mode Decomposition (EMD) with Fast Mask Convolutional Neural Network (EMD-FMCNN), to reduce the impact of noise on PCG signals.Using datasets from the NHS supplemented with Additive White Gaussian Noise (AWGN), the effectiveness of the EMD-FMCNN approach is compared to the Double-Density Discrete Wavelet Transform (DD-DWT) methodology.Evaluation metrics such as Mean Square Error (MSE) and Signal-to-Noise Ratio (SNR) are utilized.The results indicate that the EMD-FMCNN approach outperforms DD-DWT, yielding superior SNR values of 25.55 dB compared to 18.19 dB, and achieving optimal MSE values of 0.01% compared to 0.42% for DD-DWT.The findings demonstrate the effectiveness of the EMD-FMCNN approach in denoising PCG signals, offering a promising method for improving the accuracy of cardiovascular disease diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".