This study seeks to evaluate whether using non-invasive electrocardiograph (ECG) techniques, including long term ECG monitoring with wearable ECGs, can improve the detection of concealed Brugada syndrome.
Age range
18 Years – 100 Years
Sex
ALL
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Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Sensitivity, specificity, and area under the curve (AUC) of AI algorithm for detection of Brugada type 1 ECG pattern on 12-lead ECGs.
Timeframe: At completion of algorithm validation, approximately 12 months after study start
Detection rate of Brugada ECG pattern using extended-duration multi-electrode ambulatory ECG monitoring (wearable ECG) in patients with concealed Brugada syndrome.
Timeframe: Up to 12 months from enrolment
Number of cases of Brugada or Long QT Syndrome (LQTS) detected using extended-duration multi-electrode ambulatory ECG monitoring in patients with idiopathic ventricular fibrillation (VF), after application of AI ECG detection algorithms.
Timeframe: Up to 12 months from enrolment