Frontiers in cardiovascular medicine · 2025
ECG-XPLAIM: eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data.
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Pantelidis Panteleimon, Ruipérez-Campillo Samuel, Vogt Julia E, Antonopoulos Alexios, Gialamas Ioannis, Zakynthinos George E, Spartalis Michael, Dilaveris Polychronis, Millet Jose, Papaioannou Theodore G, Oikonomou Evangelos, Siasos Gerasimos
Аннотация
BACKGROUND: Timely and accurate detection of arrhythmias from electrocardiograms (ECGs) is crucial for improving patient outcomes. While artificial intelligence (AI)-based ECG classification has shown promising results, limited transparency and interpretability often impede clinical adoption.
METHODS: We present ECG-XPLAIM, a novel deep learning model dedicated to ECG classification that employs a one-dimensional inception-style convolutional architecture to capture local waveform features (e.g., waves and intervals) and global rhythm patterns. To enhance interpretability, we integrate Grad-CAM visualization, highlighting key waveform segments that drive the model's predictions. ECG-XPLAIM was trained on the MIMIC-IV dataset and externally validated on PTB-XL for multiple arrhythmias, including atrial fibrillation (AFib), sinus tachycardia (STach), conduction disturbances (RBBB, LBBB, LAFB), long QT (LQT), Wolff-Parkinson-White (WPW) pattern, and paced rhythm detection. We evaluated performance using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), and benchmarked against a simplified convolutional neural network, a two-layer gated recurrent unit (GRU), and an external, pre-trained, ResNet-based model.
RESULTS: Internally (MIMIC-IV), ECG-XPLAIM achieved high diagnostic performance (sensitivity, specificity, AUROC > 0.9) across most tasks. External evaluation (PTB-XL) confirmed generalizability, with metric values exceeding 0.95 for AFib and STach. For conduction disturbances, macro-averaged sensitivity reached 0.90, specificity 0.95, and AUROC 0.98. Performance for LQT, WPW, and pacing rhythm detection was 0.691/0.864/0.878, 0.773/0.973/0.895, and 0.96/0.988/0.993 (sensitivity/specificity/AUROC), respectively. Compared to baseline models, ECG-XPLAIM offered superior performance across most tests, and improved sensitivity over the external ResNet-based model, albeit at the cost of specificity. Grad-CAM revealed physiologically relevant ECG segments influencing predictions and highlighted patterns of potential misclassification.
CONCLUSION: ECG-XPLAIM combines high diagnostic performance with interpretability, addressing a key limitation in AI-driven ECG analysis. The open-source release of ECG-XPLAIM's architecture and pre-trained weights encourages broader adoption, external validation, and further refinement for diverse clinical applications.
METHODS: We present ECG-XPLAIM, a novel deep learning model dedicated to ECG classification that employs a one-dimensional inception-style convolutional architecture to capture local waveform features (e.g., waves and intervals) and global rhythm patterns. To enhance interpretability, we integrate Grad-CAM visualization, highlighting key waveform segments that drive the model's predictions. ECG-XPLAIM was trained on the MIMIC-IV dataset and externally validated on PTB-XL for multiple arrhythmias, including atrial fibrillation (AFib), sinus tachycardia (STach), conduction disturbances (RBBB, LBBB, LAFB), long QT (LQT), Wolff-Parkinson-White (WPW) pattern, and paced rhythm detection. We evaluated performance using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), and benchmarked against a simplified convolutional neural network, a two-layer gated recurrent unit (GRU), and an external, pre-trained, ResNet-based model.
RESULTS: Internally (MIMIC-IV), ECG-XPLAIM achieved high diagnostic performance (sensitivity, specificity, AUROC > 0.9) across most tasks. External evaluation (PTB-XL) confirmed generalizability, with metric values exceeding 0.95 for AFib and STach. For conduction disturbances, macro-averaged sensitivity reached 0.90, specificity 0.95, and AUROC 0.98. Performance for LQT, WPW, and pacing rhythm detection was 0.691/0.864/0.878, 0.773/0.973/0.895, and 0.96/0.988/0.993 (sensitivity/specificity/AUROC), respectively. Compared to baseline models, ECG-XPLAIM offered superior performance across most tests, and improved sensitivity over the external ResNet-based model, albeit at the cost of specificity. Grad-CAM revealed physiologically relevant ECG segments influencing predictions and highlighted patterns of potential misclassification.
CONCLUSION: ECG-XPLAIM combines high diagnostic performance with interpretability, addressing a key limitation in AI-driven ECG analysis. The open-source release of ECG-XPLAIM's architecture and pre-trained weights encourages broader adoption, external validation, and further refinement for diverse clinical applications.