Revealing intracavitary catheter positions: artificial intelligence-enhanced localization with an electrogram-driven variational autoencoder model

Abstract

Abstract Background The acquisition of electroanatomical (EA) maps in clinical electrophysiology procedures typically depends on locating intracardiac mapping catheters through magnetic triangulation with external patches placed on the patient's skin. However, this configuration is hampered by shifts in the reference system if the patient moves or if the setup changes during the procedure. Leveraging the feature extraction capabilities of artificial intelligence models, it becomes possible to assess catheter positions within the atria solely based on the information encoded in intracavitary electrograms (EGMs). Consequently, a neural network model could serve as an alternative or complementary system for locating EA mapping catheters, eliminating the need for external reference patches. Purpose We aimed to create a deep learning model utilizing a disentangled variational autoencoder (d-VAE) and unipolar EGM signals. The goal was to extract the precise position of a multi-electrode catheter within the left atrium and predict the specific atrial region in which it is situated, relying solely on the information derived from unipolar EGMs. Methods The model was trained using data from 51 patients with persistent atrial fibrillation who underwent pulmonary vein ablation. We exported 3D EA maps obtained with a 20-pole multi-electrode catheter, comprising a total of 40,000 unipolar EGMs with a duration of 30 seconds. Atrial geometries were segmented into 12 different areas (refer to the Figure). Subsequently, the d-VAE model was trained with unipolar EGMs to acquire encoded features, which were then input into a classifier model. The classifier was combined with atrial positions using one-hot encodings as the ground truth. The optimal architecture was selected through a grid search involving different layer numbers, sizes, and fine-tuning. For the encoding part, we assessed the negative evidence lower bound (ELBO) and Pearson’s correlation. Regarding the classifier, we evaluated accuracy, extended accuracy (indicating accuracy in predicting the region or an adjacent one), and the area under the curve (AUC). Results The best-performing autoencoder architecture presented a negative ELBO of -0.087 and a Pearson’s correlation of 0.971; and the classifier obtained an accuracy of 38.1%, an extended accuracy of 75.2%, and an AUC of 0.814. Conclusion We introduced an alternative method for localizing intracardiac catheters within the atrial chambers based on deep-learning techniques. In contrast to traditional hardware technologies, our proposed method eliminates the need for external magnetic or impedance reference patches. This approach is not limited to AF and can be extended to other supra-ventricular procedures. The results of our proposed method are promising, suggesting potential advancements in alternative systems for locating intracardiac catheters to complement existing classical hardware methods in the future.Variational autoencoder architecture