Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

Santos Rodrigues, Ana ; Augustauskas, Rytis ; Lukosevicius, Mantas ; Laguna, Pablo (Universidad de Zaragoza) ; Marozas, Vaidotas

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/s22145414

Año: 2022

Publicado en: Sensors 22, 14 (2022), [22 pp.]
ISSN: 1424-8220

Factor impacto JCR: 3.9 (2022)

Categ. JCR: CHEMISTRY, ANALYTICAL rank: 26 / 86 = 0.302 (2022) - Q2 - T1
Categ. JCR: INSTRUMENTS & INSTRUMENTATION rank: 19 / 63 = 0.302 (2022) - Q2 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 100 / 274 = 0.365 (2022) - Q2 - T2

Factor impacto CITESCORE: 6.8 - Engineering (Q1) - Chemistry (Q1) - Biochemistry, Genetics and Molecular Biology (Q2) - Physics and Astronomy (Q1)

Factor impacto SCIMAGO: 0.764 - Instrumentation (Q1) - Analytical Chemistry (Q1) - Medicine (miscellaneous) (Q2) - Information Systems (Q2) - Biochemistry (Q2) - Atomic and Molecular Physics, and Optics (Q2) - Electrical and Electronic Engineering (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R
Financiación: info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)