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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Kastor, J. A. (2000).Arrhythmias. WB Saunders Co.
[2]	Nasiri, J. A., Naghibzadeh, M., Yazdi, H. S., & Naghibzadeh, B. (2009, November). ECG arrhythmia classification with support vector machines and genetic algorithm. In Computer Modeling and Simulation, 2009. EMS'09. Third UKSim European Symposium on (pp. 187-192). IEEE.
[3]	M. Elgendi, M. Jonkman, F. D. Boer, “Premature Atrial Complexes Detection Using the Fisher Linear Discriminant”, 7th IEEE Int. Conf. on Cognitive Informatics (ICCI'08), 2008.
[4]	A. Gharaviri, F. Dehghan, M. Teshnelab, H. Abrishami,” comparision of neural network anfis, and SVM classification for PVC arrhythmia detection”, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008.
[5]	F. Melgani, Y. Bazi,” Classification of Electrocardiogram Signals with Support Vector Machines and Particle Swarm Optimization”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 12, NO. 5, SEPTEMBER, 2008.
[6]	T. H. Linh, S. Osowski, and M. L. Stodoloski, “On-line heart beat recognition using Hermite polynomials and neuron-fuzzy network,” IEEE Trans. Instrum. Meas., vol. 52, no. 4, pp. 1224–1231, Aug. 2003.
[7]	A. Azemi, V. R. Sabzevari, M. Khademi, H. Gholizadeh, A. Kiani, Z. Dastgheib,” Intelligent Arrhythmia Detection and Classification Using ICA”, Proceedings of the 28th IEEE , EMBS Annual International Conference , New York City, USA, Aug 30-Sept 3, 2006.
[8]	T. Inan, L. Giovangrandi, and J. T. A. Kovacs, “Robust neural network based classification of premature ventricular contractions using wavelet transform and timing interval features,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2507–2515, Dec. 2006.
[9]	Ceylan, R., Ozbay, Y., &Karlik, B. (2009). A novel approach for classification of ECGarrhythmias: Type-2 fuzzy clustering neural network. Expert Systems withApplications, 36, 6721–6726.
[10]	GholamHosseini, H., Luo, D., & Reynolds, K. J. (2006). The comparison of different feed forward neural network architectures for ECG signal diagnosis. Medical Engineering and Physics, 28, 372–378.
[11]	Lowitz, T., Ebert, M., Meyer, W., & Hensel, B. (2010, January). Hidden markov models for classification of heart rate variability in RR time series. In World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany (pp. 1980-1983). Springer Berlin Heidelberg.
[12]	Zhu, Y. (2012). SVM Classification Algorithm in ECG Classification. In Information Computing and Applications (pp. 797-803). Springer Berlin Heidelberg.
[13]	Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert systems with Applications, 37(8), 5751-5757.
[14]	Hu, J. L., & Bao, S. D. (2010, October). An approach to QRS complex detection based on multiscale mathematical morphology. In Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on (Vol. 2, pp. 725-729). IEEE.
[15]	Yu, S., & Chen, Y. (2007). Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognition Letters, 28, 1142–1150.
[16]	Yu, S., & Chou, K. (2009). Selection of significant independent components for ECG beat classification. Expert Systems with Applications, 36, 2088–2096
[17]	Ceylan, R., Özbay, Y., & Karlik, B. (2009). A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications, 36(3), 6721-6726.
[18]	Arif, M., Akram, M. U., & Afsar, F. A. (2009, December). Arrhythmia Beat Classification using pruned fuzzy K-nearest neighbor classifier. In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of (pp. 37-42). IEEE.
[19]	Raut, R. D., & Dudul, S. V. (2008, July). Arrhythmias Classification with MLP Neural Network and Statistical Analysis. In Emerging Trends in Engineering and Technology, 2008. ICETET'08. First International Conference on (pp. 553-558). IEEE.
[20]	Ramirez, E., Castillo, O., & Soria, J. (2010, July). Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system. In Neural Networks (IJCNN), the 2010 International Joint Conference on (pp. 1-6). IEEE.
[21]	Nambiar, V. P., Khalil-Hani, M., & Marsono, M. N. (2012, December). Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals. In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on (pp. 866-871). IEEE.
[22]	Haseena, H. H., Joseph, P. K., & Mathew, A. T. (2011). Classification of Arrhythmia Using Hybrid Networks. Journal of medical systems, 35(6), 1617-1630.
[23]	Martis, R. J., & Chakraborty, C. (2011). Arrhythmia Disease Diagnosis using Neural Network, SVM, and Genetic Algorithm-Optimized k-Means Clustering. Journal of Mechanics in Medicine and Biology, 11(04), 897-915.
[24]	Exarchos, T. P., Tsipouras, M. G., Exarchos, C. P., Papaloukas, C., Fotiadis, D. I., & Michalis, L. K. (2007). A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artificial Intelligence in Medicine, 40(3), 187-200.
[25]	Wang, J. S., Chiang, W. C., Hsu, Y. L., & Yang, Y. T. C. (2012). ECG Arrhythmia Classification Using a Probabilistic Neural Network with a Feature Reduction Method. Neurocomputing.
[26]	Zadeh, A. E., Khazaee, A., & Ranaee, V. (2010). Classification of the electrocardiogram signals using supervised classifiers and efficient features. computer methods and programs in biomedicine, 99(2), 179-194.
[27]	Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. Engineering in Medicine and Biology Magazine, IEEE, 20(3), 45-50.
[28]	Gustafson, D. E., Willsky, A. S., Wang, J. Y., Lancaster, M. C., &Triebwasser, J. H. (1978). ECG/VCG rhythm diagnosis using statistical signal analysis-I. Identification of persistent rhythms. Biomedical Engineering, IEEE Transactions on, (4), 344-353.
[29]	Vanisree, K., & Singaraj, J. (2011). Automatic Detection of ECG RR Interval using Discrete Wavelet Transformation. International Journal on Computer Science and Engineering (IJCSE).
[30]	Kumari. V. S. R., & Kumar, P. R. (2012). Performance Evaluation of Boosting Techniques for Cardiac Arrhythmia Prediction. International Journal of Computer Applications, 57(17).
[31]	Abandah, G. A., & Malas, T. M. (2010). Feature selection for recognizing handwritten Arabic letters. Dirasat Engineering Sciences Journal, 37(2).
[32]	Zhang, L. (2001). Comparison of fuzzy c-means algorithm and new fuzzy clustering and fuzzy merging algorithm. University of Nevada, Reno, NV89557.
[33]	Smith, L. I. (2002). A tutorial on principal components analysis. Cornell University, USA, 51, 52.
[34]	Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (Vol. 5, p. 767). Upper Saddle River, NJ: Prentice hall.
[35]	Correa, A., Gonzalez, A., & Ladino, C. (2011). Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case. In SAS Global Forum 2011 Data Mining and Text Analytics.
[36]	Lacerda, E., Carvalho, A. C., Braga, A. P., & Ludermir, T. B. (2005). Evolutionary radial basis functions for credit assessment. Applied Intelligence, 22(3), 167-181.
[37]	Castillo, P. A., Merelo, J. J., Prieto, A., Rivas, V., & Romero, G. (2000). G-Prop: Global optimization of multilayer perceptrons using GAs.Neurocomputing, 35(1), 149-163.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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