Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using Adaptive Focal Cross-Entropy Loss Function

Heart disease is the leading cause of mortality globally. Electrocardiograms (ECGs) are standard instruments for the examination of heart conditions, but traditional analysis is time-consuming and prone to errors. Novel advances in artificial intelligence have improved ECG classification. However, some limitations remain, such as poor interpretability, computational cost, and class imbalance. This study proposes a novel deep learning algorithm based on Depthwise Separable Residual Attention called DRA-ECG and a customized Adaptive Focal Cross-Entropy (AFCE) loss function for cardiac condition classification. This proposed methodology leverages the Continuous Wavelet Transform (CWT) method to transform 1D raw ECG signals into 2D scalograms to enhance feature representation and training. The proposed customized AFCE loss function incorporated into the DRA-ECG model addresses the class imbalance problem and boost the performance of the model. More so, this study incorporates edge feature detection as a preprocessing technique to denoise and enhance the trainable features of the 2D scalograms for optimal feature representation. The proposed DRA-ECG model achieves a high accuracy of 98.17%, recall of 95.78%, F1-score of 95.82%, and precision of 95.89%. This study shows that the results achieved by the proposed DRA-ECG surpass the current state-of-the-art and existing research works, concerning classification performance and generalization ability in ECG classification, which underlines the effectiveness of the novel AFCE loss function for ensuring high-classification accuracy and robustness. The proposed novel methodology enhances heart disease classification and provides a robust and reliable solution for medical diagnosis, addressing the major drawbacks of existing models.