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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Acknowledgements
We thank the Regional University Hospital of Malaga, Unit of Heart Clinic and Vascular Pathology, their collaboration in the review and validation of the results obtained by the proposed model. This work is part of a project funded by the Ministry of Industry, Tourism and Commerce (TSI-020302-2010-136) and University of Malaga (81434547001-3). The authors are grateful to anonymous reviewers for their valuable comments.
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Peláez, J.I., Gomez-Ruiz, J.A., Fornari, J. et al. Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems. Soft Comput 23, 4207–4219 (2019). https://doi.org/10.1007/s00500-018-3070-8
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DOI: https://doi.org/10.1007/s00500-018-3070-8