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DOI: 10.14569/IJACSA.2018.091154
PDF

Features Optimization for ECG Signals Classification

Author 1: Alan S. Said Ahmad
Author 2: Majd Salah Matti
Author 3: Adel Sabry Essa
Author 4: Omar A.M. Alhabib
Author 5: Sabri Shaikhow

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.

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Abstract: A new method is used in this work to classify ECG beats. The new method is about using an optimization algorithm for selecting the features of each beat then classify them. For each beat, twenty-four higher order statistical features and three timing interval features are obtained. Five types of beat classes are used for classification in this work, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch (LBBB) and right bundle branch (RBBB). Cuttlefish algorithm is used for feature selection which is a new bio-inspired optimization algorithm. Four classifiers are used within CFA, Scaled Conjugate Gradient Artificial Neural Network (SCG-ANN), K-Nearest Neighborhood (KNN), Interactive Dichotomizer 3 (ID3) and Support Vector Machine (SVM). The final results show an accuracy of 97.96% for ANN, 95.71% for KNN, 94.69% for ID3 and 93.06% for SVM, these results were tested on fourteen signal records from MIT-HIH database, where 1400 beats were extracted from these records.

Keywords: Features optimization; cuttlefish; ECG; ANN-SCG; ID3; KNN; SVM

Alan S. Said Ahmad, Majd Salah Matti, Adel Sabry Essa, Omar A.M. Alhabib and Sabri Shaikhow, “Features Optimization for ECG Signals Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091154

@article{Ahmad2018,
title = {Features Optimization for ECG Signals Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091154},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091154},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Alan S. Said Ahmad and Majd Salah Matti and Adel Sabry Essa and Omar A.M. Alhabib and Sabri Shaikhow}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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