The classification and identification of arrhythmias using ECG signals are of substantial practical importance in the early prevention and detection of cardiac and cardiovascular disorders. We propose an ensemble learning model to leverage the power of convolutional neural networks and transfer learning techniques to analyze heartbeat data.
We transfer CNN models on 1D heartbeat signals and employ ensemble techniques to aggregate their predictions
Our results underscore the improved accuracy of our approach in classifying ECG data, demonstrating its potential for early cardiovascular disease detection. Our method addresses the limitations of traditional ECG interpretation and offers a robust, automated approach to arrhythmia detection and classification, potentially revolutionizing the field of cardiac diagnostics. This research showcases the significance of transfer learning and ensemble learning techniques in advancing healthcare applications.