{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T09:16:08Z","timestamp":1788081368623,"version":"build-2784847793"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["PZ00P3_180055"],"award-info":[{"award-number":["PZ00P3_180055"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>Continuous 5\u2005min 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\u2005s 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\u2212). 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\u2005ms vs. 124\u2005ms, P &amp;lt; 0.001) and higher terminal force in lead V1 (2.5\u2005au vs. 1.7\u2005au, P &amp;lt; 0.01) compared with BrS\u2212 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%).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/europace\/euad334","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T22:10:06Z","timestamp":1699567806000},"source":"Crossref","is-referenced-by-count":21,"title":["Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3017-0645","authenticated-orcid":false,"given":"Beatrice","family":"Zanchi","sequence":"first","affiliation":[{"name":"Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI , Lugano , Switzerland"},{"name":"Department of Quantitative Biomedicine, University of Zurich , Zurich , Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8720-1256","authenticated-orcid":false,"given":"Francesca Dalia","family":"Faraci","sequence":"additional","affiliation":[{"name":"Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare of SUPSI , Lugano , Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1803-8237","authenticated-orcid":false,"given":"Ali","family":"Gharaviri","sequence":"additional","affiliation":[{"name":"Center for Computational Medicine in Cardiology, USI , via La Santa 1, 6900, Lugano , Switzerland"},{"name":"Centre of Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9864-1938","authenticated-orcid":false,"given":"Marco","family":"Bergonti","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale , via Tesserete 64, 6900, Lugano , Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8551-5617","authenticated-orcid":false,"given":"Tomas","family":"Monga","sequence":"additional","affiliation":[{"name":"Faculty of Biomedical Sciences, Universit\u00e0 della Svizzera Italiana (USI), Lugano, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2116-6993","authenticated-orcid":false,"given":"Angelo","family":"Auricchio","sequence":"additional","affiliation":[{"name":"Center for Computational Medicine in Cardiology, USI , via La Santa 1, 6900, Lugano , Switzerland"},{"name":"Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale , via Tesserete 64, 6900, Lugano , Switzerland"},{"name":"Faculty of Biomedical Sciences, Universit\u00e0 della Svizzera Italiana (USI), Lugano, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2248-3456","authenticated-orcid":false,"given":"Giulio","family":"Conte","sequence":"additional","affiliation":[{"name":"Center for Computational Medicine in Cardiology, USI , via La Santa 1, 6900, Lugano , Switzerland"},{"name":"Division of Cardiology, Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale , via Tesserete 64, 6900, Lugano , Switzerland"},{"name":"Faculty of Biomedical Sciences, Universit\u00e0 della Svizzera Italiana (USI), Lugano, 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