Ensemble Deep Learning Approach for ECG-Based Cardiac Disease Detection: Signal and Image Analysis

The classification and identification of arrhythmias using ECG signals hold substantial practical importance in the early prevention and detection of cardiac/cardiovascular disorders. Traditional ECG interpretation, relying on human clinical judgment, is susceptible to errors due to fatigue. Our method harnesses the power of two-dimensional convolutional neural networks (2D-CNNs) and transfer learning techniques, such as ResNet50, VGG16, and VGG19, to analyze both 2D image representations and one-dimensional heartbeat signal data. Initially, we train CNN and transfer learning models on 1D heartbeat signals and employ ensemble techniques to combine their predictions. Additionally, we construct models using transfer learning and CNN for analyzing 2D heartbeat images. By utilizing ensemble methods to consolidate the predictions of these models, we achieve a commendable accuracy of 0.94 for ECG signal classification and 0.93 for ECG image data. Our results underscore the efficiency and improved accuracy of our proposed approach in classifying ECG data, demonstrating its potential for early cardiovascular disease detection. This research showcases the significance of leveraging 2D-CNNs, transfer learning, and ensemble techniques in advancing cardiac disorder diagnosis based on ECG information.