Abstract
Cartesian Genetic programming Evolved Artificial Neural Network (CGPANN) is explored for classification of different types of arrhythmia and presented in this paper. Electrocardiography (ECG) signal is preprocessed to acquire important parameters and then presented to the classifier. The parameters are calculated from the location and amplitudes of ECG fiducial points, determined with a new algorithm inspired by Pan-Tompkins’s algorithm [14]. The classification results are satisfactory and better than contemporary methods introduced in the field.
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Ahmad, A.M., Muhammad Khan, G., Mahmud, S.A. (2013). Classification of Arrhythmia Types Using Cartesian Genetic Programming Evolved Artificial Neural Networks. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_29
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DOI: https://doi.org/10.1007/978-3-642-41013-0_29
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