Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients

• URL: http://arxiv.org/abs/2510.17172v1
• Date: Mon, 20 Oct 2025 05:26:55 GMT
• Title: Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients
• Authors: Shun Huang, Wenlu Xing, Shijia Geng, Hailong Wang, Guangkun Nie, Gongzheng Tang, Chenyang He, Shenda Hong,
• Abstract summary: Malignant ventricular arrhythmias (VT/VF) following acute myocardial infarction (AMI) are a major cause of in-hospital death.<n>This study aimed to develop a hybrid predictive framework that integrates a large-scale electrocardiogram foundation model (ECGFounder) with an interpretable XGBoost classifier.<n>We analyzed 6,634 ECG recordings from AMI patients, among whom 175 experienced in-hospital VT/VF.
• Score: 14.778478113602675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malignant ventricular arrhythmias (VT/VF) following acute myocardial infarction (AMI) are a major cause of in-hospital death, yet early identification remains a clinical challenge. While traditional risk scores have limited performance, end-to-end deep learning models often lack the interpretability needed for clinical trust. This study aimed to develop a hybrid predictive framework that integrates a large-scale electrocardiogram (ECG) foundation model (ECGFounder) with an interpretable XGBoost classifier to improve both accuracy and interpretability. We analyzed 6,634 ECG recordings from AMI patients, among whom 175 experienced in-hospital VT/VF. The ECGFounder model was used to extract 150-dimensional diagnostic probability features , which were then refined through feature selection to train the XGBoost classifier. Model performance was evaluated using AUC and F1-score , and the SHAP method was used for interpretability. The ECGFounder + XGBoost hybrid model achieved an AUC of 0.801 , outperforming KNN (AUC 0.677), RNN (AUC 0.676), and an end-to-end 1D-CNN (AUC 0.720). SHAP analysis revealed that model-identified key features, such as "premature ventricular complexes" (risk predictor) and "normal sinus rhythm" (protective factor), were highly consistent with clinical knowledge. We conclude that this hybrid framework provides a novel paradigm for VT/VF risk prediction by validating the use of foundation model outputs as effective, automated feature engineering for building trustworthy, explainable AI-based clinical decision support systems.

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