The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification - Ceylan, Rahime (International Journal of Intelligent Systems and Applications in Engineering, 2018)
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.