The study proposes a machine learning approach using a genetic algorithm to optimize a multi-layer perceptron neural network for improved ECG arrhythmia classification.
Hypothesis-generating for ECG arrhythmia classification; prospective validation required before clinical adoption.
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.
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V. S. R. Kumari (2015) studied this question.
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