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Predicting ventricular arrhythmia in myocardial ischemia using deep learning
Anna Busatto1,2,3, Jake A Bergquist1,2,3, Tolga Tasdizen1,4
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.
Heart Rhythm O2
|July 23, 2026
Summary
High-resolution epicardial electrograms can predict the timing of premature ventricular contractions (PVCs) during acute ischemia. Personalized data significantly improves prediction accuracy, paving the way for real-time monitoring tools.
Area of Science:
- Cardiovascular Physiology
- Computational Biology
- Medical Device Technology
Background:
- Myocardial ischemia poses a life-threatening risk due to ventricular arrhythmias.
- Current monitoring methods for these events are reactive, limiting preventative strategies.
Purpose of the Study:
- To investigate if high-resolution epicardial electrograms can forecast premature ventricular contractions (PVCs) during acute ischemia.
- To determine the data requirements for personalizing PVC prediction models.
Main Methods:
- Analysis of epicardial sock electrograms from porcine acute ischemia models (21 experiments, 2252 PVCs).
- Utilized a 6-layer long short-term memory network with temporal attention to predict time-to-PVC.
- Evaluated performance using cross-validation and leave-1-experiment-out testing with subject-specific fine-tuning.
Main Results:
- Prediction accuracy improved with longer signal context (MAE decreased from 6.50s to 4.73s).
- Subject-specific fine-tuning (10-15% data) significantly reduced mean MAE (by 9.6-14.6s) and improved prediction windows.
Conclusions:
- Epicardial electrograms provide predictive signatures for PVC timing during ischemia.
- Personalized adaptation of predictive models enhances generalization, supporting real-time monitoring development.
