The role of synthetic data in mitigating imbalance in deep-learning-based arrhythmia classification: a comparative study

Deep learning models applied to arrhythmia classification often encounter substantial difficulties due to the presence of class imbalance in the dataset, wherein certain types of arrhythmias appear substantially less frequently than others. This disparity can result in biased models that excel in the majority classes but underperform in the minority classes, leading to reduced overall accuracy and potentially dangerous misclassifications that could endanger lives. The Synthetic Minority Over-Sampling Technique (SMOTE) resolves these difficulties by creating artificial samples for the minority classes, thus achieving a balanced dataset. This article examines the effectiveness of using synthetic data generated by the SMOTE method to improve the performance of deep models in classifying arrhythmias from ECG signals and their scalograms. We conduct an experimental study that applies SMOTE to balance arrhythmia classes and compare it with the performance of models trained on unbalanced data. In addition, we perform a detailed comparative analysis of ten distinct deep-based methods, particularly Convolutional Neural Networks (CNNs), and evaluate their ability to extract features from scalograms. Our results show that using synthetic data obtained by SMOTE significantly improves classification accuracy, especially when applying Convolutional Neural Networks (CNNs). These findings have important implications for medical diagnosis and monitoring of cardiovascular diseases. The article also discusses related problems and promising directions for further research.