| Texto completo | |
| Autor(es): |
Sandoval, I
;
Marques, V. G.
;
Sims, J. A.
;
Rodrigo, M.
;
Guillem, M. S.
;
Salinet, J.
;
IEEE
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2021 COMPUTING IN CARDIOLOGY (CINC); v. N/A, p. 4-pg., 2021-01-01. |
| Resumo | |
In this study, we investigated the most relevant biomarkers for noninvasive classification and mechanism location in atrial tachycardia (AT), flutter (AFL) and fibrillation (AF). Biomarkers were calculated using noninvasive body surface (BSPM) dominant frequency and phase maps. We used 19 simulations of 567 to 64-lead BSPMs, from which were extracted 32 biomarkers. Biomarker ranking was performed with ANOVA, Kendall and Lasso techniques. The best four biomarkers were identified and used to classify the arrhythmias in all combinations, and the best two used for noninvasive driver localization. Arrhythmia classification accuracy was 94.74%. The feature combination which best distinguish AFfrom non-AF were meanfilament displacement and mean 01, while those that best distinguish AFL from AT were mean and SD of SP distribution. There was good agreement across ranking techniques. Mechanism location accuracy was 78.95%, with the most important biomarkers being percentage SPs within each torso division, and SD offilament histogram cluster area. This study highlights that organization relatedfeatures well identifies AFand spatial SP distribution discriminate ATfrom AFL and also it's localization. (AU) | |
| Processo FAPESP: | 18/25606-2 - Mapeamento da atividade fibrilatória cardíaca acurada: uma contribuição experimental |
| Beneficiário: | João Loures Salinet Júnior |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |