Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Abstract

Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes.

Methods: Consecutive patients who underwent catheter ablation between 2015 and 2017 with panoramic left atrial electrogram before ablation and clinical follow-up for at least 1 year following ablation were included. Convolutional neural network and a novel multimodal fusion framework were developed for predicting 1-year atrial fibrillation recurrence after catheter ablation from electrogram, ECG signals, and clinical features. The models were trained and validated using 10-fold cross-validation on patient-level splits.

Results: One hundred fifty-six patients (64.5±10.5 years, 74% male, 42% paroxysmal) were analyzed. Using electrogram signals alone, the convolutional neural network achieved an area under the receiver operating characteristics curve (AUROC) of 0.731, outperforming the existing APPLE scores (AUROC=0.644) and CHA2DS2-VASc scores (AUROC=0.650). Similarly using 12-lead ECG alone, the convolutional neural network achieved an AUROC of 0.767. Combining electrogram, ECG, and clinical features, the fusion model achieved an AUROC of 0.859, outperforming single and dual modality models.

Conclusions: Deep neural networks trained on electrogram or ECG signals improved the prediction of catheter ablation outcome compared with existing clinical scores, and fusion of electrogram, ECG, and clinical features further improved the prediction. This suggests the promise of using machine learning to help treatment planning for patients after catheter ablation.

Keywords: Machine learning; atrial fibrillation; cardiac electrophysiology; catheter ablation.

Figure 1.. (A) Overview of our methods.

The inputs come from three modalities: patient EGM signals, ECG signals, and clinical features. A multimodal machine learning model fuses the inputs from the three modalities and outputs prediction of AF recurrence. (B) Details of our multimodal fusion framework. We first trained a model on EGM signals only for AF recurrence prediction, and a separate model on ECG signals only for AF recurrence prediction. We then extracted EGM and ECG features from the respective trained models. Finally, the EGM and ECG features were concatenated with the clinical features, and were subsequently passed to a multimodal fusion model to predict AF recurrence.

Figure 2.. Clinical feature-based model interpretation.

Importance of clinical features in predicting AF recurrence using the CatBoost classifier (averaged across 10 folds). The five most important features are: left ventricular ejection fraction (LVEF), height, body mass index (BMI), weight, left atria volume from CT, and left atria surface area from CT.

Figure 3.. Receiver operating characteristics (ROC) curves of the clinical feature-based models, signal-based models, and the fusion model.

The x-axis shows the false positive rate averaged across 10 folds for each model, and the y-axis shows the true positive rate averaged across 10 folds for each model.