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Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System

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Soft Computing for Recognition Based on Biometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 312))

Abstract

In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal heartbeats. These were segmented and transformation and 3 methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani type fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.

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Ramírez, E., Castillo, O., Soria, J. (2010). Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_3

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  • DOI: https://doi.org/10.1007/978-3-642-15111-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15110-1

  • Online ISBN: 978-3-642-15111-8

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