Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

Jacek Wydrzyński; Stanisław Jankowski; Ewa Piątkowska-Janko

doi:10.1117/12.784745

28 December 2007 Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

Jacek Wydrzyński, Stanisław Jankowski, Ewa Piątkowska-Janko

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Proceedings Volume 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007; 69372E (2007) https://doi.org/10.1117/12.784745
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007, 2007, Wilga, Poland

Abstract

This paper presents the application of neural networks to the risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography. This work is based on dataset obtained from the Medical University of Warsaw. The studies were performed on one multiclass classifier and on binary classifiers. For each case the optimal number of hidden neurons was found. The effect of data preparation: normalization and the proper selection of parameters was considered, as well as the influence of applied filters. The best neural classifier contains 5 hidden neurons, the input ECG signal is represented by 8 parameters. The neural network classifier had high rate of successful recognitions up to 90% performed on the test data set.

Citation Download Citation

Jacek Wydrzyński, Stanisław Jankowski, and Ewa Piątkowska-Janko "Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography", Proc. SPIE 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007, 69372E (28 December 2007); https://doi.org/10.1117/12.784745

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KEYWORDS

Electrocardiography

Neurons

Electronic filtering

Binary data

Neural networks

Lead

Digital filtering

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