Talukder Md Alamin, Khalid Majdi, Kazi Mohsin, Muna Nusrat Jahan, Nur-E-Alam Mohammad, Halder Sajal, Sultana Nasrin
Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh.
Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
Sci Rep. 2024 Dec 5;14(1):30366. doi: 10.1038/s41598-024-81992-w.
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.
心血管心律失常以心律不齐为特征，会带来重大健康风险，包括中风和心力衰竭，因此准确的早期检测对于有效治疗至关重要。传统检测方法常常面临诸如数据集不平衡等挑战，限制了它们识别罕见心律失常类型的能力。本研究提出了一种新颖的混合方法，将ConvNeXt-X深度学习模型与先进的数据平衡技术相结合，以提高心律失常分类的准确性。具体而言，我们在麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库上评估了三种ConvNeXt变体——ConvNeXtTiny、ConvNeXtBase和ConvNeXtSmall，并结合随机过采样（RO）和合成少数类过采样技术-托梅克链接（STL）。实验结果表明，与STL相结合的ConvNeXtTiny模型实现了99.75%的最高准确率，其次是与RO相结合的ConvNeXtTiny，准确率为99.72%。STL技术始终提高了少数类检测和各模型的整体性能，ConvNeXtBase和ConvNeXtSmall的准确率分别达到99.69%和99.72%。这些发现凸显了ConvNeXt-X模型与强大的数据平衡技术相结合在实现可靠且精确的心律失常检测方面的有效性。这种方法在提高诊断准确性和支持医疗保健中的临床决策方面具有巨大潜力。
