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Ceylan, Rahime
Advanced Technology and Science (ATScience)
Computer Science & IT

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The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification Ceylan, Rahime
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637929

Abstract

The detection of effective features or data reduction is one of the phases of signal classification. In feature extraction phase, the detection of features which increase performance of classification is very important in terms of diagnosis of disease. Due to this reason, the using of an effective algorithm for feature extraction increases classification accuracy and also it decreases processing time of classifier.            In this study, two well-known dictionary learning algorithms are used to extract features of ECG signals. The features of ECG signals are extracted by using Method of Optimal Direction (MOD) and K-Singular Value Decomposition (K-SVD) and the extracted features are classified by Artificial Neural Network (ANN). Twelve different ECG signal classes which taken from MIT-BIH ECG Arrhythmia Database are used. When the obtained results are examined, it is seen that performance of classifier increases in usage of K-SVD for feature extraction. The highest classification accuracy is obtained as %98.74 with 5 nonzero elements in [20 1] feature vector, when K-SVD is used in feature extraction phase. This is the first time in literature that feature extraction based on dictionary learning is performed on 12 ECG signal classes and the extracted features are classified by ANN.
Feature Selection using FFS and PCA in Biomedical Data Classification with AdaBoost-SVM Ceylan, Rahime; Barstugan, Mucahid
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637928

Abstract

: Recently, there has been an increasing trend to propose computer aided diagnosis systems for biomedical pattern recognition. A computer aided diagnosis method, which aims higher classification accuracy, is developed to classify the biomedical dataset. This new method includes two types of machine learning algorithms: feature selection and classification. In this method, firstly, features were extracted from biomedical dataset, then the extracted features were classified by hybrid AdaBoost-Support Vector Machines (SVM) classifier structure. For feature selection, Forward Feature Selection (FFS) and Principal Component Analysis (PCA) algorithms were used. Following it, advantages and disadvantages of these algorithms were evaluated. The proposed two different hybrid structures and other studies in literature were compared with our findings. Wisconsin Breast Cancer (WBC), Pima Diabetes (PD), Heart (Statlog) biomedical datasets and Electrocardiogram (ECG) signals were taken from UCI database and these datasets were used to test the proposed hybrid structure. The obtained results show that the proposed hybrid structure has high classification accuracy for biomedical data classification.
Co-Authors Barstugan, Mucahid