TY - GEN
T1 - Non-invasive Mechanism Classification and Localization in Supraventricular Cardiac Arrhythmias
AU - Sandoval, I.
AU - Marques, V.G.
AU - Sims, J.A.
AU - Rodrigo, M.
AU - Guillem, M.S.
AU - Salinet, J.
N1 - Funding Information:
VOM is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 860974. IS, JAS and JS are supported by grant #2018/25606-2, Sao Paulo Research Foundation (FAPESP).
Publisher Copyright:
© 2021 Creative Commons.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.23919/CinC53138.2021.9662944
DO - 10.23919/CinC53138.2021.9662944
M3 - Conference article in proceeding
SN - 978-1-6654-6721-6
T3 - Computing in Cardiology
BT - 2021 COMPUTING IN CARDIOLOGY (CinC)
PB - IEEE
T2 - Conference on Computing in Cardiology (CinC)
Y2 - 12 September 2021 through 15 September 2021
ER -