A Multi-Center Trained Residual Neural Network for Robust Classification of Atrial High-Rate Episodes in Remotely Monitored Pacemakers and Defibrillators.

Remote monitoring of pacemakers and defibrillators increases patient safety but also increases clinical workload. Review of atrial high-rate episodes is particularly demanding as episodes can contain atrial tachycardia or atrial fibrillation (AT/AF), noise, or far-field oversensing (FFO). Automatic review of atrial high-rate episodes by an Artificial Intelligence (AI) model can decrease the workload of remote monitoring, provided it maintains high sensitivity for true atrial tachycardia. A residual network is trained using a center-level fourfold cross validation. The four resulting models achieved a precision of 97.2-99.4% for AT/AF, 93.1-97.7% for noise, and 75.4-94.4% for FFO, while maintaining high sensitivity 98.9-99.3% for AT/AF. The four models were combined through averaging prediction probabilities to create an ensemble model. Thresholding ensemble predictions with probability > 95% resulted in a robust ensemble model that made only two errors (<0.1%) after reviewing 3925 episodes (91.9%) of the total 4271 episodes. This shows how AI models can reliably assist in remote monitoring. Future research should be aimed at classification models for other episode types and clinical validation of AI models to assist remote monitoring of pacemakers and defibrillators.