Abstract
Myocardial ischemia is caused by a lack of oxygen and nutrients to the contractile cells and may lead to myocardial infarction with its severe consequence of heart failure and arrhythmia. An electrocardiogram (ECG) represents a recording of changes occurring in the electrical potentials between different sites on the skin as a result of the cardiac activity. Since the ECG is recorded easily and non–invasively, it becomes very important to provide means of reliable ischemia detection. Ischemic changes of the ECG frequently affect the entire repolarization wave shape. In this paper we propose a new classification methodology that draws from the disciplines of clustering and artificial neural networks, and apply it to the problem of myocardial ischemia detection. The results obtained are promising.
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© 2004 Springer-Verlag Berlin Heidelberg
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Tasoulis, D.K., Vladutu, L., Plagianakos, V.P., Bezerianos, A., Vrahatis, M.N. (2004). Online Neural Network Training for Automatic Ischemia Episode Detection. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_166
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DOI: https://doi.org/10.1007/978-3-540-24844-6_166
Publisher Name: Springer, Berlin, Heidelberg
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