doi:10.3390/s22145414engSantos Rodrigues, AnaAugustauskas, RytisLukosevicius, MantasLaguna, PabloMarozas, VaidotasDeep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGsART-2022-130352The 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.2022http://zaguan.unizar.es/record/12394810.3390/s22145414http://zaguan.unizar.es/record/123948oai:zaguan.unizar.es:123948info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17Rinfo:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00Sensors 22, 14 (2022), [22 pp.]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess