ArticleEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023
Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.
Article 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. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- High-density atrial mapping, P-wave analysis, and computational simulations in Brugada syndrome: Enhancing the understanding of atrial fibrillation.Heart rhythm O2 · 2025Article
- The Utility of Notched P-Wave on the Occurrence of Ventricular Fibrillation in Patients With Brugada Syndrome.Journal of arrhythmia · 2025Article
- Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation.European heart journal. Digital health · 2025Article
- Brugada syndrome.Nature reviews. Disease primers · 2025Review
- Cardiac Channelopathies: Clinical Diagnosis and Promising Therapeutics.Journal of the American Heart Association · 2025Review
- Artificial intelligence for Brugada syndrome diagnosis and gene variants interpretation.American journal of cardiovascular disease · 2025Review
- Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG.NPJ cardiovascular health · 2024Article
- aTrial arrhythmias in inhEriTed aRrhythmIa Syndromes: results from the TETRIS study.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 · 2024Article
- Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.Sports (Basel, Switzerland) · 2024Review
- The role for ambulatory electrocardiogram monitoring in the diagnosis and prognostication of Brugada syndrome: a sub-study of the Rare Arrhythmia Syndrome Evaluation (RASE) Brugada study.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 · 2024Article
- Implantable loop recorders in patients with Brugada syndrome: the BruLoop study.European heart journal · 2024Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
aimsBrugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P-wave features through an artificial intelligence (AI)-based model. METHODS AND
resultsContinuous 5 min 12-lead ECG recordings were obtained in sinus rhythm from (i) patients with spontaneous or ajmaline-induced BrS and no history of AF and (ii) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 s each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different AI-based supervised classifiers. A total of 2228 averaged P-wave observations, resulting from the analysis of 33 420 P-waves, were obtained from 123 patients (79 BrS+ and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based model training. The BrS+ patients presented with longer P-wave duration (136 ms vs. 124 ms, P < 0.001) and higher terminal force in lead V1 (2.5 au vs. 1.7 au, P < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%).
conclusionAn 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.
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