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

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((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.

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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 .

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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

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Ramírez, E., Melin, P., Prado-Arechiga, G. (2020). Hybrid Model Based on Neural Networks and Fuzzy Logic for 2-Lead Cardiac Arrhythmia Classification. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_14

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