Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification
Volume 3, Issue 5, September 2015, Pages: 150-157
Received: Apr. 16, 2015; Accepted: Apr. 25, 2015; Published: Sep. 6, 2015
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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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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.
Arrhythmia Classification, Electrocardiogram (ECG), RR Interval, MIT-BIH ECG Dataset, Multi-layer Perceptron Neural Network, Genetic Algorithm (GA)
To cite this article
V. S. R. Kumari, P. Rajesh Kumar, Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification, Communications. Vol. 3, No. 5, 2015, pp. 150-157. doi: 10.11648/j.com.20150305.21
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