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

V. S. R. Kumari; P. Rajesh Kumar

doi:doi:10.11648/j.com.20150305.21

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

V. S. R. Kumari, P. Rajesh Kumar

Published in Communications (Volume 3, Issue 5)

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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TY  - JOUR
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
PY  - 2015
N1  - https://doi.org/10.11648/j.com.20150305.21
DO  - 10.11648/j.com.20150305.21
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 150
EP  - 157
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20150305.21
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

V. S. R. Kumari

Departments of Electronics and Communication, Research scholar, Andra University, Vishakhapatnam, India
P. Rajesh Kumar

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

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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}
}

Copy | Download

TY  - JOUR
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
PY  - 2015
N1  - https://doi.org/10.11648/j.com.20150305.21
DO  - 10.11648/j.com.20150305.21
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 150
EP  - 157
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20150305.21
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  -

Copy | Download