Cagla SarvanNalan OzkurtSarvan, CaglaOzkurt, Nalan2025-10-062018978-1-5386-1501-097815386150102165-060810.1109/SIU.2018.84044232-s2.0-85050810212https://gcris.yasar.edu.tr/handle/123456789/6146https://doi.org/10.1109/SIU.2018.8404423To identify appropriate features in classification studies is a common problem in many areas. In this study a genetic algorithm method with multi-objective approach is proposed for selecting the features that give high performance ratio in classifying cardiac arrhythmia. Discrete Wavelet Transform (DWT) were used for extracting features from Normal right bundle branch block left bundle branch block and paced rhythm recordings of electrocardiography (ECG) signals which were taken from the MIT-BIH cardiac arrhythmia database. Using 13 different wavelet types 208 features were obtained by the DWT method. Among these features a minimum number of feature sets were chosen to provide high performance in classification. Then the classification results were compared with the results of the classical genetic algorithm which aims to improve accuracy.Turkishinfo:eu-repo/semantics/closedAccessECG Beat classification, arrhythmia, discrete wavelet transform, wavelet features, feature selection, neural network, genetic algorithm, multi-objective optimizationGenetic AlgorithmArrhythmiaWavelet FeaturesDiscrete Wavelet TransformECG Beat ClassificationMulti-Objective OptimizationNeural NetworkFeature SelectionConference Object