The effect of dictionary learning on weight update of AdaBoost and ECG classification

Abstract

A signal can be represented by sparse representation with fewer coefficients. Due to this ability, sparse representation is used in research fields such as signal compression, noise elimination, and classification. In this study, sparse coefficients of the signals were obtained by using dictionary learning and sparse representation algorithms. The obtained coefficients were used in the weight update process of three different classifiers, which were created by using AdaBoost, SVM, and LDA algorithms. So, Dictionary learning based AdaBoost classifiers were obtained. The proposed Dictionary Learning (DL) based AdaBoost classifiers classified the ECG (Electrocardiography) signals. Before classification, the feature selection process was applied to ECG signals and six different feature subsets were obtained by Discrete Wavelet Transform (DWT), First Order Statistics (FOS), T-test, Bhattacharyya, Entropy, and Wilcoxon test methods. The feature subsets were used as the new dataset. The classification process was done by the proposed method and satisfying results were obtained. The best classification accuracy was obtained as 99.75% by the proposed dictionary learning based method called as DL-AdaBoost-SVM on feature subsets obtained by DWT and Wilcoxon test methods.

Keywords

• AdaBoost
• Dictionary learning
• ECG
• Feature subsets
• Signal classification
• Sparse representation