The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

Journal: BMC cardiovascular disorders
Published Date: Oct 13, 2021

Abstract

BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthermore, although various ECG patterns are described in the literature, different individual ECG may show high-grade variability, making the diagnosis problematic. The study aims to develop an innovative system for an accurate diagnosis of Type 1 BrS based on ECG pattern recognition by Machine Learning (ML) models and blood markers analysis trough transcriptomic techniques.

Authors

  • M A Morales
    CNR Institute of Clinical Physiology, Via Giuseppe Moruzzi 1, 56124, Pisa, Italy.
  • M Piacenti
    Fondazione Toscana Gabriele Monasterio, Via G. Moruzzi 1, Pisa, Italy.
  • M Nesti
    U.O.C. Cardiologia Ospedale San Donato, Via Pietro Nenni 20, Arezzo, Italy.
  • G Solarino
    Azienda Usl Toscana Nord Ovest U.O.C. Cardiologia Ospedale Versilia, SS1 Via Aurelia 335, Lido di Camaiore, Italy.
  • P Pieragnoli
    Azienda Ospedaliera Universitaria Careggi SOD Aritmologia, Largo Brambilla, 3, Firenze, Italy.
  • G Zucchelli
    Azienda Ospedaliero Universitaria Pisana Cardiologia 2 U.O.C. Cisanello, Via Paradisa, 2, Pisa, Italy.
  • S Del Ry
    CNR Institute of Clinical Physiology, Via Giuseppe Moruzzi 1, 56124, Pisa, Italy.
  • M Cabiati
    CNR Institute of Clinical Physiology, Via Giuseppe Moruzzi 1, 56124, Pisa, Italy.
  • F Vozzi
    CNR Institute of Clinical Physiology, Via Giuseppe Moruzzi 1, 56124, Pisa, Italy. vozzi@ifc.cnr.it.

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