ECG Classification Performing Feature Extraction Automatically Using a Hybrid CNN-SVM Algorithm

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Atıf İçin Kopyala

Özaltın Ö., Yeniay M. Ö.

IEEE, 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), cilt.2021, sa.10.1109/HORA52670.2021.9461295, ss.1-5, 2021 (Scopus)

• Yayın Türü: Makale / Kısa Makale
• Cilt numarası: 2021 Sayı: 10.1109/HORA52670.2021.9461295
• Basım Tarihi: 2021
• Doi Numarası: 10.1109/hora52670.2021.9461295.
• Dergi Adı: IEEE, 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)
• Derginin Tarandığı İndeksler: Scopus
• Sayfa Sayıları: ss.1-5
• Hacettepe Üniversitesi Adresli: Evet

Özet

In this study, we have presented a hybrid Convolution Neural Network (CNN)-Support Vector Machine (SVM) algorithm which has overcome overfitting 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.