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.
Zanchi, B, Faraci, FD, Gharaviri, A, Bergonti, M, Monga, T, Auricchio, A & Conte, G 2023, 'Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach', Europace, vol. 25, no. 11, euad334, pp. 1-9. https://doi.org/10.1093/europace/euad334
Zanchi, B., Faraci, F. D., Gharaviri, A., Bergonti, M., Monga, T., Auricchio, A., & Conte, G. (2023). Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach. Europace, 25(11), 1-9. Article euad334. https://doi.org/10.1093/europace/euad334
@article{97b8ce53d0a44d7b863436fd269b4a66,
title = "Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach",
abstract = "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.",
author = "Beatrice Zanchi and Faraci, {Francesca Dalia} and Ali Gharaviri and Marco Bergonti and Tomas Monga and Angelo Auricchio and Giulio Conte",
note = "Funding Information: Conflict of interest: A.A. is a consultant to Boston Scientific, Cairdac, Corvia, Microport CRM, EPD Philips, and Radcliffe Publisher. He received speaker fees from Boston Scientific, Medtronic, and Microport. He participates in clinical trials sponsored by Boston Scientific, Medtronic, and EPD-Philips. He has intellectual properties with Boston Scientific, Biosense Webster, and Microport CRM. G.C. has received a research grant (PZ00P3_180055) from the Swiss National Science Foundation (SNSF), research grants from Boston Scientific Inc. and consultancy fees from Bristol Myers Squibb. All other co-authors do not report conflict of interest. Funding Information: This study was supported by a research grant of the Swiss National Science Foundation (SNSF) (PZ00P3_180055). Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology.",
year = "2023",
month = nov,
day = "2",
doi = "10.1093/europace/euad334",
language = "English",
volume = "25",
pages = "1--9",
journal = "Europace",
issn = "1099-5129",
publisher = "Oxford University Press",
number = "11",
}