Examinando por Autor "Melgarejo-Meseguer, Francisco M."

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Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure
(Elsevier, 2023) Martinez-Mateu, Laura; Melgarejo-Meseguer, Francisco M.; Muñoz-Romero, Sergio; Gimeno-Blanes, Francisco Javier; García-Alberola, Arcadi; Rocher-Ventura, Sara; Saiz, Javier; Rojo-Álvarez, José Luis
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.
Online automatic detection of phrenic nerve activation during cryoablation procedure for atrial fibrillation treatment
(Elsevier, 2025-03) Gil-Izquierdo, Antonio; Mateos-Gaitán, Roberto; Melgarejo-Meseguer, Francisco M.; Gimeno-Blanes, F. Javier; Lozano-Paredes, Dafne; Sánchez-Muñoz, Juan José; García-Alberola, Arcadi; Rojo-Álvarez, José Luis
Abstract Background and Aim: Cryoballoon ablation is an effective technique for treating Atrial Fibrillation (AF). Its application in the pulmonary vein antrum poses a potential risk of phrenic nerve damage due to its anatomic proximity. Manual protocols are implemented during the ablation procedure to mitigate this risk, although these may be susceptible to subjectivity and variations. In this work, we propose an online system capable of automatically detecting the phrenic nerve integrity during the cryoablation procedure for AF in the pulmonary veins. The system performs digital processing of the ECG signals recorded during the ablation process, detects and segments the ECG signals, and uses a machine learning classifier to infer the risk of damage. Methods: The used dataset consisted of monitoring system signals obtained from the cryoablation procedures of ten AF patients from Virgen de la Arrixaca University Clinical Hospital in Murcia, Spain. The first stage involves signal processing of the ECG leads, using noise filtering and delineation to unmask any residual cellular potential during phrenic nerve stimulation. A comparative analysis was conducted where the electrocatheter was placed near the phrenic nerve to stimulate it and when the electrocatheter was intentionally displaced, resulting in the phrenic nerve not being stimulated despite an electrical pulse being applied. The detection stage used a linear support vector classifier for both scenarios. Results: It was possible to automatically classify the level of muscle activity from the phrenic nerve with high accuracy in this known-solution dataset. An online system was created capable of performing and synchronizing all the described stages to manage the signal extracted from the monitoring system. Conclusion: The system presented here can be a valuable tool for clinical practice, enabling the identification of specific pacing pulses when phrenic nerve involvement occurs, eventually and probably minimizing the use of manual protocols subject to interpretation biases.