Key result
A 3D-CNN model predicts AF recurrence after catheter ablation with ~81% accuracy.
Why the study?
Catheter ablation is an important treatment for AF, but recurrence rates remain high.
Does a 3D-CNN model with temporal attention based on body-surface potential mapping signals improve the prediction of AF recurrence in persistent AF patients undergoing catheter ablation?
Observational (n=14)
No
Does a 3D-CNN model with temporal attention based on body-surface potential mapping signals improve the prediction of AF recurrence in persistent AF patients undergoing catheter ablation?
Effect estimate: AUC 0.88
A 3D-CNN model with temporal attention based on body-surface potential mapping signals can successfully predict the recurrence of atrial fibrillation after catheter ablation.
Catheter ablation has become an important treatment for atrial fibrillation (AF), but its recurrence rate is still high. The aim of this study was to predict AF recurrence using a three-dimensional (3D) network model based on body-surface potential mapping signals (BSPMs). BSPMs were recorded with a 128-lead vest in 14 persistent AF patients before undergoing catheter ablation (Maze-IV). The torso geometry was acquired and meshed by point cloud technology, and the BSPM was interpolated into the torso geometry by the inverse distance weighted (IDW) method to generate the isopotential map. Experiments show that the isopotential map of BSPMs can reflect the propagation of the electrical wavefronts. The 3D isopotential sequence map was established by combining the spatial-temporal information of the isopotential map; a 3D convolutional neural network (3D-CNN) model with temporal attention was established to predict AF recurrence. Our study proposes a novel attention block that focuses the characteristics of atrial activations to improve sampling accuracy. In our experiment, accuracy (ACC) in the intra-patient evaluation for predicting the recurrence of AF was 99.38%. In the inter-patient evaluation, ACC of 3D-CNN was 81.48%, and the area under the curve (AUC) was 0.88. It can be concluded that the dynamic rendering of multiple isopotential maps can not only comprehensively display the conduction of cardiac electrical activity on the body surface but also successfully predict the recurrence of AF after CA by using 3D isopotential sequence maps.
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Zhong et al. (2022) conducted an observational in Atrial fibrillation (n=14). 3D-CNN with temporal-attention block vs. Standard CNN models (LeNet, ResNet) was evaluated on Accuracy of predicting AF recurrence (inter-patient evaluation) (AUC 0.88). A 3D-CNN model with a temporal-attention block predicted atrial fibrillation recurrence after catheter ablation with an accuracy of 81.48% and an AUC of 0.88.
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