| Peer-Reviewed

Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification

Received: 16 April 2015     Accepted: 25 April 2015     Published: 6 September 2015
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Abstract

An Electrocardiogram (ECG) graphically records changes in electrical potentials between different sites on the skin due to cardiac activity. The heart’s electrical activity is a depolarization and depolarization sequence. ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods. This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum.

Published in Communications (Volume 3, Issue 5)
DOI 10.11648/j.com.20150305.21
Page(s) 150-157
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Arrhythmia Classification, Electrocardiogram (ECG), RR Interval, MIT-BIH ECG Dataset, Multi-layer Perceptron Neural Network, Genetic Algorithm (GA)

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  • APA Style

    V. S. R. Kumari, P. Rajesh Kumar. (2015). Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification. Communications, 3(5), 150-157. https://doi.org/10.11648/j.com.20150305.21

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    ACS Style

    V. S. R. Kumari; P. Rajesh Kumar. Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification. Communications. 2015, 3(5), 150-157. doi: 10.11648/j.com.20150305.21

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    AMA Style

    V. S. R. Kumari, P. Rajesh Kumar. Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification. Communications. 2015;3(5):150-157. doi: 10.11648/j.com.20150305.21

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  • @article{10.11648/j.com.20150305.21,
      author = {V. S. R. Kumari and P. Rajesh Kumar},
      title = {Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification},
      journal = {Communications},
      volume = {3},
      number = {5},
      pages = {150-157},
      doi = {10.11648/j.com.20150305.21},
      url = {https://doi.org/10.11648/j.com.20150305.21},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20150305.21},
      abstract = {An Electrocardiogram (ECG) graphically records changes in electrical potentials between different sites on the skin due to cardiac activity. The heart’s electrical activity is a depolarization and depolarization sequence. ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods. This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum.},
     year = {2015}
    }
    

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    T1  - Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification
    AU  - V. S. R. Kumari
    AU  - P. Rajesh Kumar
    Y1  - 2015/09/06
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    N1  - https://doi.org/10.11648/j.com.20150305.21
    DO  - 10.11648/j.com.20150305.21
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    AB  - An Electrocardiogram (ECG) graphically records changes in electrical potentials between different sites on the skin due to cardiac activity. The heart’s electrical activity is a depolarization and depolarization sequence. ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods. This study proposes multi-layer perceptron neural network optimization using Genetic Algorithm (GA) to classify ECG arrhythmia. Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum.
    VL  - 3
    IS  - 5
    ER  - 

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Author Information
  • Departments of Electronics and Communication, Research scholar, Andra University, Vishakhapatnam, India

  • Departments of Electronics and Communication, Andra University, Vishakhapatnam, India

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