Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach.

Autor:	Zanchi B; Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI, Lugano, Switzerland.; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Faraci FD; Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI, Lugano, Switzerland., Gharaviri A; Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland.; Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland., Bergonti M; Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland., Monga T; Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland., Auricchio A; Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland.; Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland.; Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland., Conte G; Center for Computational Medicine in Cardiology, USI, via La Santa 1, 6900, Lugano, Switzerland.; Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, via Tesserete 64, 6900, Lugano, Switzerland.; Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland.
Jazyk:	angličtina
Zdroj:	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 [Europace] 2023 Nov 02; Vol. 25 (11).
DOI:	10.1093/europace/euad334
Abstrakt:	Aims: Brugada 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 Results: Continuous 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%). Conclusion: 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. (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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