Hybrid Deep Learning Structure for Classifying ECG Signals Using CNN, MVO-Based Feature Selection, and SVM

Abstract

An accurate and effective heart disease diagnosis through the electrocardiogram (ECG) analysis remains the crucial issue in clinical practice due to complexity and nonlinearity of ECG patterns, morphological variability, and noise interference. This study proposes a hybrid framework that combines a convolutional neural network (CNN) with a support vector machine (SVM) for ECG signal classification. In the proposed hybrid model, feature extraction was done with CNN. Then, a multi-verse optimizer was used for feature selection. The classifier of the hybrid model is an SVM. The proposed hybrid model is trained and evaluated on the standard publicly available QT database (QTDB). Spectrogram and scalogram images generated from individual ECG beats were used as input to the proposed model. The 512 features obtained with spectrogram and scalogram images decreased to 259 features. The same dataset was used to determine the classification accuracy, which was 99.29% for scalogram-based images and 92.12% for spectrogram-based images. This notable enhancement shows that, in comparison to spectrograms, scalogram representations offer better distinguish features for classification of ECG signals. Moreover, a proposed method outperformed existing techniques. A paired t-test confirmed that proposed method significantly improved accuracy compared with CNN+SVM (p < 0.0001).