123948 20240319081011.0 doi 10.3390/s22145414 sideral 130352 ART-2022-130352 eng Santos Rodrigues, Ana Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs 2022 Access copy available to the general public Unrestricted The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector''s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4 degrees and 7.3 degrees) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology. info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00 info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.9 2022 CHEMISTRY, ANALYTICAL 26 / 86 = 0.302 2022 Q2 T1 INSTRUMENTS & INSTRUMENTATION 19 / 63 = 0.302 2022 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 100 / 274 = 0.365 2022 Q2 T2 0.764 2022 Instrumentation 2022 Q1 Analytical Chemistry 2022 Q1 Medicine (miscellaneous) 2022 Q2 Information Systems 2022 Q2 Biochemistry 2022 Q2 Atomic and Molecular Physics, and Optics 2022 Q2 Electrical and Electronic Engineering 2022 Q2 6.8 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Augustauskas, Rytis Lukosevicius, Mantas Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Marozas, Vaidotas 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 22, 14 (2022), [22 pp.] Sensors Sensors 1424-8220 4844012 http://zaguan.unizar.es/record/123948/files/texto_completo.pdf Versión publicada 2703716 http://zaguan.unizar.es/record/123948/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:123948 articulos driver 2024-03-18-15:09:51 ARTICLE