Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.

Giorgio LuongoGaetano VacantiVincent NitzkeDeborah NairnClaudia NagelDiba KabiriTiago P AlmeidaDiogo C SorianoMassimo W RivoltaGhulam André NgOlaf Dössel Armin LuikRoberto SassiClaus SchmittAxel Loewe

Published in: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology (2022)

Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Keyphrases

machine learning
atrial fibrillation
artificial intelligence
big data
catheter ablation
deep learning
left atrial