A Modular Neural Network Approach for Cardiac Arrhythmia Classification

  • Eduardo Ramírez
  • Patricia MelinEmail author
  • German Prado-Arechiga
Part of the Studies in Computational Intelligence book series (SCI, volume 862)


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.


Modular neural network Multilayer perceptron 12-lead arrhythmia classification 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Eduardo Ramírez
    • 1
  • Patricia Melin
    • 1
    Email author
  • German Prado-Arechiga
    • 1
  1. 1.Tijuana Institute of Technology, Graduate StudiesTijuanaMexico

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