Localizing the Origin of Idiopathic Ventricular Arrhythmias From ECG Using a Recurrent Convolutional Neural Network With Attention
Bibliographic record
Abstract
Idiopathic ventricular arrhythmias (IVAs) are extra abnormal heartbeats disturbing the regular heart rhythm that can become fatal if left untreated. Cardiac catheter ablation is the standard approach to treating IVAs; however, a crucial prerequisite for the ablation is the localization of the IVA origin. The current IVA localization techniques are invasive, rely on expert interpretation, or are inaccurate. This study aims to develop a deep learning algorithm that can automatically identify the origin of IVAs from electrocardiogram (ECG) signals without the need for expert manual analysis. Our developed deep learning algorithm comprises a spatial feature extraction to extract the most informative features from multichannel ECG data, temporal modeling to capture the evolving pattern of the ECG time series, and an attention mechanism to weigh the most important temporal features and improve the model’s interpretability. The algorithm was validated on a 12-lead ECG dataset collected from 334 patients (230 females, age$46~\pm ~13$) who experienced IVAs and successfully underwent a catheter ablation procedure that determined the IVAs’ exact origins. It achieved an area under the curve (AUC) of 93%, an accuracy of 94%, a sensitivity of 97%, a precision of 95%, and an${F}1$-score of 96% in locating the origin of IVAs and outperformed existing automatic and semi-automatic algorithms. Our proposed method shows promise toward automatic and noninvasive evaluation of IVA patients before cardiac catheter ablation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".