Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays

Riccio, Jennifer (Universidad de Zaragoza) ; Alcaine, Alejandro ; Rocher, Sara ; Martinez-Mateu, Laura ; Saiz, Javier ; Invers-Rubio, Eric ; Guillem, Maria S. ; Martínez, Juan Pablo (Universidad de Zaragoza) ; Laguna, Pablo (Universidad de Zaragoza)

Resumen: Atrial fbrosis plays a key role in the initiation and progression of atrial fbrillation (AF). Atrial fbrosis is typically identifed by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets. Nevertheless, this approach disregards signal spatiotemporal information and b-EGM sensitivity to catheter orientation. To overcome these limitations, we propose the dominant-to-remaining eigenvalue dominance ratio (EIGDR) of unipolar electrograms (u-EGMs) within neighbor electrode cliques as a waveform dispersion measure, hypothesizing that it is correlated with the presence of fbrosis. A simulated 2D tissue with a fbrosis patch was used for validation. We computed EIGDR maps from both original and time-aligned u-EGMs, denoted as R and RA, respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, ΔRA. The performance of each map in detecting fbrosis was evaluated
in scenarios including noise and variable electrode-tissue distance. Best results were achieved by RA, reaching 94% detection accuracy, versus the 86% of b-EGMs voltage maps. The proposed strategy was also tested in real u-EGMs from fbrotic and non-fbrotic areas over 3D electroanatomical maps, supporting the ability of the EIGDRs as fbrosis markers, encouraging further studies to confrm their translation to clinical settings.

Idioma: Inglés

DOI: 10.1007/s11517-022-02648-3

Año: 2022

Publicado en: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 60, 11 (2022), 3091-3112
ISSN: 0140-0118

Factor impacto JCR: 3.2 (2022)

Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 17 / 55 = 0.309 (2022) - Q2 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 18 / 31 = 0.581 (2022) - Q3 - T2
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 60 / 110 = 0.545 (2022) - Q3 - T2
Categ. JCR: ENGINEERING, BIOMEDICAL rank: 56 / 96 = 0.583 (2022) - Q3 - T2

Factor impacto CITESCORE: 6.1 - Computer Science (Q1) - Engineering (Q1)

Factor impacto SCIMAGO: 0.653 - Computer Science Applications (Q2) - Biomedical Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/Construyendo Europa desde Aragón
Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group
Financiación: info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA
Financiación: info:eu-repo/grantAgreement/EC/H2020/860974/EU/Personalized Therapies for Atrial Fibrillation. A Translational Approach/PersonalizeAF
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-105674RB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)