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Pushpa S
Department of Computer Science and Engineering, St.Peter's Institute f Higher Education and Research,Chennai

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Arrhythmia Classification Based on Combined Chaotic and Statistical Feature Extraction Jayagopi G; Pushpa S
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp127-136

Abstract

Obvious information content in Electro cardio graph has become mandatory to reveal the abnormalities in the heart functions. Arrhythmia is commonly seen heart disorder and results in fatal end, if not identified and treated properly within time limits. The straight forward scene in such diagnosis is to detect the salient features from the Electro cardio graph data using signal processing methods followed by proper classification methods.  16 classes of Arrhythmia had been classified in this work by adopting the traditional method of abnormality detection while introducing a novelty in the type of features to be extracted. Lyapunov Exponents, Kolmogorov Sinai Entropy Density, Kolmogorov Sinai Entropy Universality and R-R interval features based on Kurtosis and Skewness had been used to classify the heart beats from the benchmark MIT-Arrhythmia database. Since alternative features had been utilized, common Support Vector Machines based classification could produce an accuracy of 98.95% in the proposed work with just 13 features.
Co-Authors Jayagopi G