RT Journal Article
T1 Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems
A1 Peláez Sánchez, José Ignacio
A1 Gómez-Ruiz, José Antonio
A1 Fornari, Javier
A1 Vaccaro-Witt, Gustavo Fabian
K1 Electrocardiografía
AB 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.
PB Springer-Verlag
YR 2019
FD 2019
LK https://hdl.handle.net/10630/28772
UL https://hdl.handle.net/10630/28772
LA eng
NO Soft Computing. Vol. 23(12): 4207-4219. 2019
NO Spanish project TSI-020302-2010-136University of Málaga: 81434547001-3
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026