Innovative ECG Classification Approach Utilizing a Transfer Learning-Driven Ensemble Architecture

Abstract

An electrocardiogram (ECG/EKG) is a vital methodology that is used for the diagnosis and monitoring of heart diseases by recording the electrical activity of the heart. However, manual analysis of ECGs shows limitations such as noise sensitivity, visual interpretation constraints and data imbalance. The proposed study a deep learning ensemble model combining DenseNet121, InceptionV3, and ResNet50 are implement to classify ECG images to improve diagnostic accuracy. The model is trained on two datasets: the National Heart Foundation 2023 ECG dataset and the ECG Dataset for Heart Condition Classification, focusing the main cardiac conditions such as abnormal heartbeat, myocardial infarction. The preprocessing techniques include background removal of ECG signal images, grayscale conversion, and data augmentation to enhance image quality and overfitting reduction. Stratified 5-Fold cross-validation was employed to demonstrate the generalization abilities of the proposed models. Early stopping and performance plots demonstrated that proposed model is not overfitting and two proposed models show consistent accuracy which suggests the model is not biased toward a specific dataset. While the ensemble models, as demonstrated in this study, produce better results than single models. The proposed study demonstrates validation accuracies of 98.62% and 96.75% for the National Heart Foundation 2023 dataset and the ECG dataset for heart condition classification, respectively, using 5-fold stratified crossvalidation. There are still some limitations, such as the proposed ensemble models not being evaluated using Explainable AI, which reduces clinical trust. Additionally, small datasets can limit the model's generalizability. Therefore, this study demonstrates the potential of deep ensemble models with advanced preprocessing for ECG classification, but it also highlights the importance of greater transparency, better dataset diversity, and real-world validation in future research studies.