131594 20250829134152.0 doi 10.22489/CinC.2023.342 sideral 136865 ART-2023-136865 eng Ramírez, Julia Universidad de Zaragoza (orcid)0000-0003-4130-5866 A Multi-layer CNN Using the ECG, Age and Sex Predicts Ventricular Arrhythmias in the General Population 2023 Access copy available to the general public Unrestricted Life-threatening ventricular arrhythmias (LTVA) prediction in individuals without cardiovascular disease remains a major challenge. We tested the performance of a multilayer convolutional neural network (CNN) using ECG signals, age and sex. We split 86,603 individuals from the UK Biobank study into a training (90%) and a test (10%) set. In the training set, we trained a multilayer CNN using 15-second ECGs at rest from lead I, age and sex as inputs. The output was the probability of LTVA within a 12-year follow-up. The CNN model consisted of a four-layer CNN (128, 128, 256 and 256 channels, kernel sizes of 3) and a single attention layer. Age and sex were included as external inputs to the final layer. In the test set (0.9% LTVA events), the CNN's prediction led to a median AUC of 0.601, and a specificity of 0.287 for a sensitivity of 0.750. We set a threshold at the CNN's prediction value maximising the sum of specificity and sensitivity in the training set. Survival analyses showed a hazard ratio (HR) of 1.396(P=0.021) for individuals with a CNN's prediction value > threshold, versus those with a CNN's prediction value < threshold. A multilayer CNN model using 10-second ECG data from lead I, together with information on age and sex, can stratify individuals at risk of LTVA. Our findings support the potential utility of wearables for accessible screening in the general population. info:eu-repo/grantAgreement/ES/AEI/PID2021-128972OA-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00 info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130459B-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.227 2023 Computer Science (miscellaneous) 2023 Cardiology and Cardiovascular Medicine 2023 1.1 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Miguel, Antonio Universidad de Zaragoza (orcid)0000-0001-5803-4316 van Duijvenboden, Stefan Orini, Michele Young, William J. Tinker, Andrew Lambiase, Pier D. Munroe, Patricia B. Martínez, Juan Pablo Universidad de Zaragoza (orcid)0000-0002-7503-3339 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 50 (2023), [4 pp.] Comput. cardiol. Computing in Cardiology 2325-8861 190471 http://zaguan.unizar.es/record/131594/files/texto_completo.pdf Versión publicada 2640792 http://zaguan.unizar.es/record/131594/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:131594 articulos driver 2025-08-29-13:39:57 ARTICLE