Why the study?
Does a hybrid CNN-SVM algorithm improve accuracy in classifying ECG signals compared to CNN or SVM alone?
Does a hybrid CNN-SVM algorithm improve accuracy in classifying ECG signals compared to CNN or SVM alone?
A hybrid CNN-SVM algorithm (ProposedNet-SVM) achieves high accuracy (99.524%) in classifying ECG signals transformed into 2D images.
May enhance ECG classification in research datasets; leaves open prospective clinical validation before practice use.
In this study, we have presented a hybrid Convolution Neural Network (CNN)-Support Vector Machine (SVM) algorithm which has overcome overtitting for classifying Electrocardiogram (ECG) signals that have been transformed to 2D images using continuous wavelet transform (CWT). We also have suggested ProposedNet that is a kind of convolutional neural network algorithm. Also, it has been trained more than once. Moreover, it has performed feature extraction automatically. We have compared the ProposedNet which has 34 layers, with SVM. Additionally, we also have compared ProposedNet-SVM that is also our suggestion, with these algorithms. Comparison results indicate that ProposedNet, SVM, and ProposedNet-SVM have been achieved accuracy rates of 95.6%, 89.17%, and 99.524% respectively.
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Özaltın et al. (2021) studied this question.
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