Automatic identification of characteristic points related to pathologies in electrocardiograms to design expert systems
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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.
KeywordsECG Cardiovascular disease Bundle branch blocks Medical diagnosis Multilayer perceptron
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.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
- Bayés de Luna A (2006) Semiología electrocardiográfica II: Patrones diagnósticos de crecimiento, bloqueos y preexcitación. Prous Science, BarcelonaGoogle Scholar
- GholamHosseini H, Nazeran H, Moran B (1998) ECG compression: evaluation of FFT, DCT, and WT performance. Australas Phys Eng Sci Med 21(4):186–192Google Scholar
- Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215–220. https://doi.org/10.1161/01.CIR.101.23.e215 CrossRefGoogle Scholar
- Haykin S (2008) Neural networks and learning machines, 3er edn. Pearson, LondonGoogle Scholar
- He L, Hou W, Zhen X, Peng C (2006) Recognition of ECG patterns using artificial neural network. In: Sixth international conference on intelligent systems design and applications, pp 477–481. EEE, Jinan. https://doi.org/10.1109/ISDA.2006.253883
- Mallet S (1999) A wavelet tour of signal processing. Academic Press, San DiegoGoogle Scholar
- Marple SL (1987) Digital spectral analysis with applications. Prentice Hall, Englewood CliffsGoogle Scholar
- So HH, Chan KL (1997) Development of QRS detection method for real-time ambulatory cardiac monitor. In: Proceedings of the 19th annual international conference of the IEEE engineering in medicine and biology society. IEEE, Chicago, IL, pp 289–292. https://doi.org/10.1109/IEMBS.1997.754529