Accurate detection of cardiac abnormalities from electrocardiogram recordings is regarded as essential for clinical diagnostics and decision support. Traditional deep learning models such as residual networks and transformer architectures have been applied successfully to this task, but their performance has been limited when long sequential signals are processed. Recently, state space models have been introduced as an efficient alternative. In this study, a hybrid framework named One Dimensional Convolutional Neural Network Electrocardiogram Mamba is introduced, in which convolutional feature extraction is combined with Mamba, a selective state space model designed for effective sequence modeling. The model is built upon Vision Mamba, a bidirectional variant through which the representation of temporal dependencies in electrocardiogram data is enhanced. Comprehensive experiments on the PhysioNet Computing in Cardiology Challenges of 2020 and 2021 were conducted, and superior performance compared with existing methods was achieved. Specifically, the proposed model achieved substantially higher AUPRC and AUROC scores than those reported by the best previously published algorithms on twelve lead electrocardiograms. These results demonstrate the potential of Mamba-based architectures to advance reliable ECG classification. This capability supports early diagnosis and personalized treatment, while enhancing accessibility in telemedicine and resource-constrained healthcare systems.

翻译：从心电图记录中准确检测心脏异常被认为是临床诊断和决策支持的关键。残差网络和Transformer架构等传统深度学习模型已成功应用于此任务，但在处理长序列信号时性能受限。近来，状态空间模型作为一种高效替代方案被引入。本研究提出了一种名为一维卷积神经网络心电图Mamba的混合框架，其中卷积特征提取与Mamba（一种专为高效序列建模而设计的选择性状态空间模型）相结合。该模型基于Vision Mamba（一种双向变体）构建，可增强心电图数据中时间依赖关系的表征能力。在2020年和2021年PhysioNet计算心脏病学挑战赛数据集上进行的综合实验表明，该模型相比现有方法取得了更优性能。具体而言，所提模型在12导联心电图上获得的AUPRC和AUROC评分显著高于此前最佳公开算法报告的结果。这些结果证明了基于Mamba的架构在推进可靠心电图分类方面的潜力。该能力可支持早期诊断和个性化治疗，同时提升远程医疗和资源受限医疗系统的可及性。