Improvement Performance Deep Learning-Based Multi-Class ECG Classification Model with Limited Medical Dataset

Real-world medicl data, such as electrocardiogram (ECG), often exhibits imbalanced data distributions, which presents a considerable difficulty for classification tasks. the use of augmented data remains controversial in the clinical field, as it is not real and may introduce biases into the training data learned by the generative model. In this study, we proposed a method for achieving optimal results without relying on augmentation techniques. The experiments included using class weight (Experiment A-1), focal loss (Experiment A-2), balancing all classes to match the smallest class (Experiment B), and configuring subclasses (Experiment C). We employed the ResNet deep learning model for multi-class classification. Experiment A used either focal loss or class weight and achieved high scores of 0.95 and 0.93 respectively. Focal loss had the best performance among the two methods. We developed a method to improve the performance of an ECG classifier with limited data. Results show that properly weighting the loss function, specifically using focal loss, in a deep learning model is more effective than altering the amount of data to solve the issue of imbalanced data.