Enhanced Classification of ECG Heartbeats through Data Augmentation and Convolutional Neural Networks for Diagnostic Accuracy - G. Rama Koteswara Rao, Chithirala Harsha Vardhan, V. V. Satyanarayana Challa, Kolli Meghanadh, Maredla Gowshik (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In light of the persistent global prevalence of cardiovascular diseases (CVDs), accurate and efficient early detection and intervention. This research delves into the realm of high-accuracy ECG heartbeat analysis, employing a robust convolutional neural network (CNN) approach with data augmentation to enhance classification performance. exploring a diverse range of heartbeat categories, including normal beats, unknown beatsbeats, and fusion beats. To address data imbalances and improve model generalization, a strategic data augmentation technique is implemented, focusing on upsampling minority classes. designed with multiple convolutional layers, batch normalization, and max-pooling, providing a comprehensive framework for feature extraction and pattern recognition in ECG signals. The model is trained and evaluated using key metrics such as accuracy and loss. Furthermore, the research showcases the augmentation on model performance and presents a detailed analysis . The investigation extends to the interpretation of model predictions, offering valuable insights into the distinguishing features of different heartbeat categories. The experimental results demonstrate the efficacy of the proposed approach in achieving high accuracy for heartbeat classification. This study contributes to cardiovascular health by presenting an advanced methodology for ECG heartbeat analysis, emphasizing the significance of data augmentation and CNNs in achieving superior classification results. The findings not only provide a foundation for high-accuracy diagnostic models but also offer a basis for future research and development in the domain of cardiovascular disease prediction