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Identification of Brugada Syndrome Based on P-Wave Features: An Artificial Intelligence-Based Approach.

Beatrice ZanchiFrancesca D FaraciAli GharaviriMarco BergontiTomas MongaAngelo AuricchioGiulio Conte
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 (2023)
An AI machine learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis.
Keyphrases
  • artificial intelligence
  • machine learning
  • big data
  • deep learning
  • minimally invasive
  • atrial fibrillation
  • case report
  • left atrial
  • bioinformatics analysis
  • heart failure