(AF risk) A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
Background & Objective
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Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality.
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Its early detection is challenging because of the low detection yield of conventional methods.
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Objective: We aimed to develop a deep learning‑based algorithm to identify AF during normal sinus rhythm (NSR) using 12‑lead electrocardiogram (ECG) findings.
Methods & Key Findings
We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12‑lead ECGs.
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Data Analysis: Raw digital data of 2,412 12‑lead ECGs were analyzed.
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Key Discovery: The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12‑lead ECG.
Validation & Model Performance
We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI‑based algorithm showed the following values:
| Metrics | Internal | External |
| AUROC | 0.79 | 0.75 |
| Recall | 82% | 77% |
| Specificity | 78% | 72% |
| F1 Score | 75% | 74% |
| Overall Accuracy | 72.8% | 71.2% |
Conclusion
The deep learning‑based algorithm using 12‑lead ECG demonstrated high accuracy for detecting AF during NSR.