000123948 001__ 123948
000123948 005__ 20240319081011.0
000123948 0247_ $$2doi$$a10.3390/s22145414
000123948 0248_ $$2sideral$$a130352
000123948 037__ $$aART-2022-130352
000123948 041__ $$aeng
000123948 100__ $$aSantos Rodrigues, Ana
000123948 245__ $$aDeep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
000123948 260__ $$c2022
000123948 5060_ $$aAccess copy available to the general public$$fUnrestricted
000123948 5203_ $$aThe 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.
000123948 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R$$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00
000123948 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000123948 590__ $$a3.9$$b2022
000123948 592__ $$a0.764$$b2022
000123948 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1
000123948 593__ $$aInstrumentation$$c2022$$dQ1
000123948 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1
000123948 593__ $$aAnalytical Chemistry$$c2022$$dQ1
000123948 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2
000123948 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2
000123948 593__ $$aInformation Systems$$c2022$$dQ2
000123948 593__ $$aBiochemistry$$c2022$$dQ2
000123948 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2
000123948 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2
000123948 594__ $$a6.8$$b2022
000123948 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000123948 700__ $$aAugustauskas, Rytis
000123948 700__ $$aLukosevicius, Mantas
000123948 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000123948 700__ $$aMarozas, Vaidotas
000123948 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000123948 773__ $$g22, 14 (2022), [22 pp.]$$pSensors$$tSensors$$x1424-8220
000123948 8564_ $$s4844012$$uhttps://zaguan.unizar.es/record/123948/files/texto_completo.pdf$$yVersión publicada
000123948 8564_ $$s2703716$$uhttps://zaguan.unizar.es/record/123948/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000123948 909CO $$ooai:zaguan.unizar.es:123948$$particulos$$pdriver
000123948 951__ $$a2024-03-18-15:09:51
000123948 980__ $$aARTICLE