Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure.


Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
03 2023
Historique:
received: 27 09 2022
revised: 17 01 2023
accepted: 08 02 2023
pubmed: 23 2 2023
medline: 15 3 2023
entrez: 22 2 2023
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 36812811
pii: S0010-4825(23)00120-8
doi: 10.1016/j.compbiomed.2023.106655
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

106655

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A. García-Alberola declares institutional research and tuition fees from Medtronic Ibérica.

Auteurs

Laura Martinez-Mateu (L)

Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain. Electronic address: laura.martinez.mateu@urjc.es.

Francisco M Melgarejo-Meseguer (FM)

Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain.

Sergio Muñoz-Romero (S)

Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain; D!lemmaLab Ltd Startup, Fuenlabrada, Spain.

Francisco-Javier Gimeno-Blanes (FJ)

D!lemmaLab Ltd Startup, Fuenlabrada, Spain; Departamento de Ingeniería de Comunicaciones, Universidad Miguel Hernández, Elche, Spain.

Arcadi García-Alberola (A)

Unidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca - IMIB, Murcia, Spain.

Sara Rocher-Ventura (S)

Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.

Javier Saiz (J)

Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain.

José Luis Rojo-Álvarez (JL)

Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain; D!lemmaLab Ltd Startup, Fuenlabrada, Spain.

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