Predicting ventricular arrhythmia in myocardial ischemia using deep learning

<h2>Abstract</h2><h3>Background</h3> Myocardial ischemia can trigger ventricular arrhythmias with life-threatening consequences. Current monitoring is largely reactive, limiting opportunities for preventive intervention. <h3>Objective</h3> To determine whether high-resolution epicardial electrograms contain predictive signatures that enable forecasting the timing of premature ventricular contractions (PVCs) during acute ischemia, and to quantify subject-specific data requirements for effective personalization. <h3>Methods</h3> We analyzed epicardial sock electrograms (247 electrodes, 1 kHz) from 21 porcine acute ischemia experiments comprising 2,252 spontaneous PVCs. Signals were segmented into overlapping sequences of 3, 5, or 7 consecutive non-PVC beats with a continuous target of time-to-next PVC. A 6-layer Long Short-Term Memory (LSTM) network (hidden size 128) with temporal attention was trained using mean absolute error (MAE). Performance was evaluated in (A) pooled 80/10/10 cross-validation and (B) leave-one-experiment-out testing with subject-specific fine-tuning using 10% or 15% of held-out data. <h3>Results</h3> In Paradigm A, MAE decreased with longer context (6.50 s for 3 beats, 5.97 s for 5 beats, 4.73 s for 7 beats) with excellent calibration (<mml:math><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>></mml:mo><mml:mn>0.996</mml:mn></mml:mrow></mml:math>). In Paradigm B, increasing fine-tuning from 10% to 15% reduced mean MAE by 9.6–14.6 s and flattened error growth with prediction horizon, improving the fraction of predictions within 30–60 s windows. <h3>Conclusion</h3> Epicardial electrograms support accurate PVC time-to-event forecasting during acute ischemia, and modest subject-specific adaptation substantially improves generalization, motivating development of real-time predictive monitoring tools.