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Catheter ablation of persistent atrial fibrillation guided by electrograms spatiotemporal dispersion : automatic identification using machine learning approaches

Abstract : Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent patient-tailored AF ablation therapy, giving 95% of procedural success rate, is based on the use of a multipolar mapping catheter called PentaRay. It targets areas of spatiotemporal dispersion (STD) in the atria as potential AF drivers. STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads.In practice, interventional cardiologists localize STD sites visually using the PentaRay multipolar mapping catheter. This thesis aims to automatically characterize and identify ablation sites in STD-based ablation of persistent AF using machine learning (ML) including deep learning (DL) techniques. In the first part, EGM recordings are classified into STD vs. non-STD groups. However, highly imbalanced dataset ratio hampers the classification performance. We tackle this issue by using adapted data augmentation techniques that help achieve good classification. The overall performance is high with values of accuracy and AUC around 90%. First, two approaches are benchmarked, feature engineering and automatic feature extraction from a time series, called maximal voltage absolute values at any of the bipoles (VAVp). Statistical features are extracted and fed to ML classifiers but no important dissimilarity is obtained between STD and non-STD categories. Results show that the supervised classification of raw VAVp time series itself into the same categories is promising with values of accuracy, AUC, sensi-tivity and specificity around 90%. Second, the classification of raw multichannel EGM recordings is performed. Shallow convolutional arithmetic circuits are investigated for their promising theoretical interest but experimental results on synthetic data are unsuccessful. Then, we move forward to more conventional supervised ML tools. We design a selection of data representations adapted to different ML and DL models, and benchmark their performance in terms of classification and computational cost. Transfer learning is also assessed. The best performance is achieved with a convolutional neural network (CNN) model for classifying raw EGM matrices. The average performance over cross-validation reaches 94% of accuracy and AUC added to an F1-score of 60%. In the second part, EGM recordings acquired during mapping are labeled ablated vs. non-ablated according to their proximity to the ablation sites then classified into the same categories. STD labels, previously defined by interventional cardiologists at the ablation procedure, are also aggregated as a prior probability in the classification task.Classification results on the test set show that a shallow CNN gives the best performance with an F1-score of 76%. Aggregating STD label does not help improve the model’s performance. Overall, this work is among the first attempts at the application of statistical analysis and ML tools to automatically identify successful ablation areas in STD-based ablation. By providing interventional cardiologists with a real-time objective measure of STD, the proposed solution offers the potential to improve the efficiency and effectiveness of this fully patient-tailored catheter ablation approach for treating persistent AF.
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Submitted on : Tuesday, September 7, 2021 - 11:17:10 AM
Last modification on : Thursday, September 9, 2021 - 3:34:33 AM


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  • HAL Id : tel-03336517, version 1



Amina Abdelkader. Catheter ablation of persistent atrial fibrillation guided by electrograms spatiotemporal dispersion : automatic identification using machine learning approaches. Automatic. Université Côte d'Azur, 2021. English. ⟨NNT : 2021COAZ4026⟩. ⟨tel-03336517⟩



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