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A Modular Neural Network Approach for Cardiac Arrhythmia Classification

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

In this work we describe a modular neural network approach to use expert modules as a classification model for 12-lead cardiac arrhythmias. The modular neural network is designed using Multilayer perceptron as classifiers. This modular neural network was trained and tested with the Physilalisch-Technische Bundesantalt diagnostic ECG database (PTB database) of physioBank. The electrocardiograms are preprocessed to improve their classification through the proposed modular neural network. This modular neural network uses the features extracted of each signal such as autoregressive model coefficients, Shannon entropy and multifractal wavelets. We used the twelve electrode signals or leads included in the PTB database, such as i, ii, iii, avf, avr, avl, v1, v2, v3, v4, v5, v6, vx, vy and vz. The modular neural network is composed by twelve expert modules, where each module is used to perform the classification for the specific signal lead. The expert modules are based on the following models: multilayer perceptron with scaled conjugate gradient backpropagation (MLP-SCG). Finally, the outputs from the expert modules are combined using winner-takes-all integration as modular neural network integration method.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Graduate Studies, Tijuana, BC, Mexico

    Eduardo Ramírez, Patricia Melin & German Prado-Arechiga

Authors
  1. Eduardo Ramírez
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  2. Patricia Melin
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  3. German Prado-Arechiga
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Ramírez, E., Melin, P., Prado-Arechiga, G. (2020). A Modular Neural Network Approach for Cardiac Arrhythmia Classification. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_18

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