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

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.

Methods

The study is structured in 3 parts: (a) a retrospective study, with the first cohort of 300 anonymized ECG obtained in already diagnosed Type 1 BrS (75 s

SUBMITTER: Morales MA 

PROVIDER: S-EPMC8513180 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature