Key result
Hybrid 1D-CNN-RHNN models achieve ~97% accuracy in classifying cardiovascular diseases from ECG signals.
Why the study?
Manual analysis of ECGs for CVD detection is laborious and subject to interobserver variability, motivating automated categorization approaches.
Hybrid deep learning models combining 1D-CNN with RHNN or ResNet can accurately classify multiple cardiovascular diseases from ECG signals.
High-accuracy hybrid CNN models support ECG-based multi-class CVD detection; extends deep learning validation for automated cardiac diagnostics.
ardio Vascular Diseases (CVDs) pose an important global health challenge, contributing substantially to mortality rates worldwide. Electrocardiography (ECG) is a necessary diagnostic tool in the detection of CVDs. Manual analysis by medical experts, for ECG interpretation, is laborious and subject to interobserver variability. To overcome these limitations, automated categorization technique has gained prominence, enabling efficient CVDs classification. The major focus of this work is to utilize deep learning (DL) approach for the identification of CVDs using ECG signals. The presented work incorporates two hybrid models: one-dimensional convolutional neural network (1D-CNN) with Recurrent Hopfield Neural Network (1DCNN-RHNN) and with Residual Network (1D-CNN-ResNet), to obtain important features from raw data and categorize them into different groups that correlate to CVD situation. The 1D-CNN-RHNN model achieved classification accuracy of 96.62% in the 4-class classification of normal, coronary artery disease (CAD), myocardial infarction (MI), and congestive heart failure (CHF) and the 1DCNN-ResNet model achieved classification accuracy of 95.75% in the 5-class classification of normal, CAD, MI, CHF and cardiomyopathy. The proposed model's functionality is validated with medical ECG data, and its outcomes are evaluated using various measures. Experimental findings demonstrate that the proposed models outperform other existing approaches in categorizing multiple classes. Our suggested approach might potentially help doctors screen for CVDs using ECG signals and is capable of being verified with larger databases.
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Padmavathi et al. (2024) studied Cardiovascular Diseases. Hybrid deep learning models (1D-CNN-RHNN and 1D-CNN-ResNet) vs. Other existing approaches was evaluated on Classification accuracy. The 1D-CNN-RHNN model achieved a classification accuracy of 96.62% in 4-class categorization, and the 1D-CNN-ResNet model achieved 95.75% in 5-class categorization of cardiovascular diseases using ECG signals.
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