Abstract
Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants-ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall-combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare.