Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure
Background/Aim: In atrial fibrillation (AF) ablation procedures, it is desirable to know whether a proper disconnection of the pulmonary veins (PVs) was achieved. We hypothesize that information about their isolation could be provided by analyzing changes in P-wave after ablation. Thus, we present a method to detect PV disconnection using P-wave signal analysis. Methods: Conventional P-wave feature extraction was compared to an automatic feature extraction procedure based on creating low-dimensional latent spaces for cardiac signals with the Uniform Manifold Approximation and Projection (UMAP) method. A database of patients (19 controls and 16 AF individuals who underwent a PV ablation procedure) was collected. Standard 12-lead ECG was recorded, and P-waves were segmented and averaged to extract conventional features (duration, amplitude, and area) and their manifold representations provided by UMAP on a 3-dimensional latent space. A virtual patient was used to validate these results further and study the spatial distribution of the extracted characteristics over the whole torso surface. Results: Both methods showed differences between P-wave before and after ablation. Conventional methods were more prone to noise, P-wave delineation errors, and inter-patient variability. P-wave differences were observed in the standard leads recordings. However, higher differences appeared in the torso region over the precordial leads. Recordings near the left scapula also yielded noticeable differences. Conclusions: P-wave analysis based on UMAP parameters detects PV disconnection after ablation in AF patients and is more robust than heuristic parameterization. Moreover, additional leads different from the standard 12-lead ECG should be used to detect PV isolation and possible future reconnections better.
This work was partially supported by research Grant PID2019-104356RB-C41, PID2019-104356RB-C42, PID2019-104356RB-C43, and PID2019-106623RB-C41 funded by MCIN/AEI/10.13039/501100011033, MCIN/AEI/10.13039/501100011033,and by research Grant PROMETEO/2020/043 funded by Generalitat Valenciana. It was also partially supported by Grant SC-LEARNING-CM funded by Next Generation REACT-UE and Grant 2023/00004/032-F924 and 2023/00004/030-F923 funded by Universidad Rey Juan Carlos
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