ECG‐TransCovNet: A hybrid transformer model for accurate arrhythmia detection using Electrocardiogram signals
Bibliographic record
Abstract
Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading to severe cardiac conditions and sudden cardiac deaths. Therefore, early and accurate detection of arrhythmias is crucial for timely intervention and potentially life‐saving treatment. Artificial Intelligence, particularly deep learning, has revolutionised the detection and diagnosis of various health conditions, including arrhythmias. A unique hybrid architecture, ECG‐TransCovNet, that combines Convolutional Neural Networks and Transformer models for enhanced arrhythmia detection in Electrocardiogram signals is introduced. The authors’ approach leverages the superior temporal pattern recognition capabilities of Transformers and the spatial feature extraction strengths of convolutional neural networks, providing a robust and accurate solution for arrhythmia detection. The performance and generalisability of the authors’ proposed model are validated through tests on the MIT‐BIH arrhythmia and PhysioNet databases. The authors conducted experimental trials using these two benchmark datasets. The authors’ results demonstrate that the proposed ECG‐TransCovNet model achieves state‐of‐the‐art (SOTA) performance in terms of detection accuracy, reaching 98.6%. Additionally, the authors conducted several experiments and compared the results to the most recent techniques utilising their assessment measures. The experimental results demonstrate that the authors’ model can generally produce better results.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".