000118839 001__ 118839
000118839 005__ 20230914083537.0
000118839 0247_ $$2doi$$a10.3390/computation10070112
000118839 0248_ $$2sideral$$a130066
000118839 037__ $$aART-2022-130066
000118839 041__ $$aeng
000118839 100__ $$aAguiar-Salazar, E.
000118839 245__ $$aRapid detection of cardiac pathologies by neural networks using ECG signals (1D) and sECG images (3D)
000118839 260__ $$c2022
000118839 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118839 5203_ $$aUsually, 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.
000118839 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000118839 592__ $$a0.386$$b2022
000118839 593__ $$aComputer Science (miscellaneous)$$c2022$$dQ2
000118839 593__ $$aTheoretical Computer Science$$c2022$$dQ3
000118839 593__ $$aModeling and Simulation$$c2022$$dQ3
000118839 593__ $$aApplied Mathematics$$c2022$$dQ3
000118839 594__ $$a3.3$$b2022
000118839 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118839 700__ $$aVillalba-Meneses, F.
000118839 700__ $$aTirado-Espín, A.
000118839 700__ $$aAmaguaña-Marmol, D.
000118839 700__ $$aAlmeida-Galárraga, D.
000118839 773__ $$g10, 7 (2022), 112 [14 pp]$$pComputation$$tComputation$$x2079-3197
000118839 8564_ $$s7787541$$uhttps://zaguan.unizar.es/record/118839/files/texto_completo.pdf$$yVersión publicada
000118839 8564_ $$s2860516$$uhttps://zaguan.unizar.es/record/118839/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118839 909CO $$ooai:zaguan.unizar.es:118839$$particulos$$pdriver
000118839 951__ $$a2023-09-13-13:19:05
000118839 980__ $$aARTICLE