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15-02-2018 | Methodologies and Application

Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems

Authors: Jose Ignacio Peláez, Jose Antonio Gomez-Ruiz, Javier Fornari, Gustavo F. Vaccaro

Published in: Soft Computing | Issue 12/2019

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Abstract

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.

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Title: Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems
Authors: Jose Ignacio Peláez
Jose Antonio Gomez-Ruiz
Javier Fornari
Gustavo F. Vaccaro
Publication date: 15-02-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 12/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3070-8

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