Rapid detection of cardiac pathologies by neural networks using ECG signals (1D) and sECG images (3D)

Aguiar-Salazar, E. ; Villalba-Meneses, F. ; Tirado-Espín, A. ; Amaguaña-Marmol, D. ; Almeida-Galárraga, D.

Resumen: Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. On the other hand, surface electrocardiography (sECG) is a little-explored technique for this diagnosis. sECGs are three-dimensional images (two dimensions in space and one in time). In this way, the signals were taken in one-dimensional format and analyzed using neural networks. Following the transformation of the one-dimensional signals to three-dimensional signals, they were analyzed in the same sense. For this research, two models based on LSTM and ResNet34 neural networks were developed, which showed high accuracy, 98.71% and 93.64%, respectively. This study aims to propose the basis for developing Decision Support Software (DSS) based on machine learning models. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Idioma: Inglés

DOI: 10.3390/computation10070112

Año: 2022

Publicado en: Computation 10, 7 (2022), 112 [14 pp]
ISSN: 2079-3197

Factor impacto CITESCORE: 3.3 - Mathematics (Q2) - Computer Science (Q2)

Factor impacto SCIMAGO: 0.386 - Computer Science (miscellaneous) (Q2) - Theoretical Computer Science (Q3) - Modeling and Simulation (Q3) - Applied Mathematics (Q3)

Tipo y forma: Artículo (Versión definitiva)