2026-06-02T10:38:11Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/287722026-02-03T10:54:59Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Peláez Sánchez, José Ignacio author Gómez-Ruiz, José Antonio author Fornari, Javier author Vaccaro-Witt, Gustavo Fabian author 2019 Electrocardiograms (ECG) record the electrical activity of the heart through 12 main signals called shunts. Medical experts examine certain segments of these signals in where they believe the cardiovascular disease is manifested. This fact is an important determining factor for designing expert systems for cardiac diagnosis, as it requires the direct expert opinion in order to locate these specific segments in the ECG. The main contributions of this paper are: (i) to propose a model that uses the full ECG signal to identify key characteristic points that define cardiac pathology without medical expert intervention and (ii) to present an expert system based on artificial neural networks capable of detecting bundle branch block disease using the previous approach. Cardiologists have validated the proposed model application and a comparative analysis is performed using the MIT-BIH arrhythmia database. Soft Computing. Vol. 23(12): 4207-4219. 2019 https://hdl.handle.net/10630/28772 10.1007/s00500-018-3070-8 Electrocardiografía Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems