Multimodal hierarchical masked autoencoders tailored to ECG time series for atrial fibrillation risk stratification

Cianci , Giuseppe

Multimodal hierarchical masked autoencoders tailored to ECG time series for atrial fibrillation risk stratification

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Cianci , G. Multimodal hierarchical masked autoencoders tailored to ECG time series for atrial fibrillation risk stratification. 2025. handle: http://hdl.handle.net/10230/71795

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Abstract

Background: Atrial fibrillation affects 26% of Europeans over 40, with catheter ablation being a primary treatment. Low voltage areas significantly influence ablation success, but their identification currently requires invasive electroanatomical mapping during the procedure. Objective: We developed a deep learning framework to predict finegrained, region-specific low voltage areas from electrocardiograms and clinical data, enabling pre-procedural risk stratification. Methods: We collected data from 138 atrial fibrillation patients undergoing catheter ablation at University Hospital Basel, with external validation cohorts from Lausanne (n=20) and Bern (n=9). Our multimodal architecture processes ECG signals through pretrained backbones (ST-MEM, CRNN, DenseNet) and clinical features through separate branches, fusing them for hierarchical multilabel classification across 8 atrial regions, 2 area thresholds, and 4 voltage thresholds (64 targets total). Key innovations include: (1) adaptation of max constraint loss to logit space for numerical stability, (2) parallel multi-lead QRS detection for robust heartbeat segmentation, and (3) region-specific adaptive learning rate scheduling. Results: Our model achieved an AUROC of 0.782 for general low voltage area prediction, outperforming models trained on 10 times more data from previous studies. A single model is enough to predict 64 classes of low voltage areas, which is a significant improvement over previous studies. Conclusions: Non-invasive low voltage area prediction from routine ECGs is feasible and clinically relevant. The hierarchical multilabel framework provides detailed spatial information about atrial substrate, potentially enabling better patient selection and procedural planning for atrial fibrillation ablation.

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Treball fi de màster de: Erasmus Mundus joint Master in Artificial Intelligence (EMAI)
Supervisors: Prof. Dr. Julia E. Vogt (ETH Zurich) Samuel Ruipérez-Campillo (ETH Zurich) Co-supervisors: Prof. Vicenç Gómez Cerdà (Universitat Pompeu Fabra) Dr. Sven Knecht (University Hospital Basel)