Automated ECG arrhythmia classification using hybrid CNN-SVM architectures

Diagnosing cardiac diseases relies heavily on electrocardiogram (ECG) analysis, but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations. Despite advancements in machine learning, achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue. Computer-aided diagnosis systems can play a key role in early detection, reducing mortality rates associated with cardiac disorders. This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time. The methodology consists of three stages: 1) Preprocessing, where ECG signals undergo noise reduction and feature extraction; 2) Feature Identification, where deep convolutional neural network (CNN) blocks, combined with data augmentation and transfer learning, extract key parameters; and 3) Classification, where a hybrid CNN-SVM model is employed for arrhythmia recognition. CNN-extracted features were fed into a binary support vector machine (SVM) classifier, and model performance was assessed using five-fold cross-validation. Experimental findings demonstrated that the CNN2 model achieved 85.52% accuracy, while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%, outperforming conventional methods. This model enhances classification efficiency while reducing computational complexity. The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification, offering a promising solution for real-time clinical applications. Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.