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Hybrid Model Based on Neural Networks and Fuzzy Logic for 2-Lead Cardiac Arrhythmia Classification

  • Eduardo Ramírez
  • Patricia MelinEmail author
  • German Prado-Arechiga
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 827)

Abstract

We present how to combine neural networks and fuzzy logic to form a hybrid model as a classification system using 2-lead for cardiac arrhythmias. This hybrid model is used samples of electrocardiograms contained in the MIT-BIH arrhythmia database. The samples of heartbeats are extracted and transformed from the electrocardiograms of this database. The hybrid model is trained and tested with 10 different classes of normal and cardiac arrhythmias heartbeats. The hybrid model used 2 leads included in the MIT-BIH arrhythmia database. The hybrid model used two basic module units, where each unit processing one lead. The basic module unit are composite by three classifiers. Finally, we combined the output results of the two basic module unit with a fuzzy system and we have achievement increase the global classification rate in the hybrid model proposed.

Keywords

Fuzzy KNN algorithm Neural network Fuzzy system 2-lead arrhythmia classification Hybrid model 

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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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