Xinbo-Liu / Siamese-Neural-Networks-for-ECG-signalLinks
本研究基于孪生网络和N-way K-shot学习策略,创新提出了一种结合transformer的心电信号分类架构,命名为SMC-Net(Siamese Meta-learning Contrastive Neural Network)。该模型通过孪生网络的结构特性和元学习的策略,显著提升了对新类别心电图模式的快速适应性。在公开的SPH数据集上,对26种心脏疾病的心电图信号进行了全面评估。通过细致的实验设计,本研究全面考察了模型在准确率、敏感性、特异性及F1分数等关键性能指 标上的表现,其中在小样本测试环境中,模型分别达到了90.91%、100%、95.24%和95%的最高性能表现。结果显示,尽管面临样本数量的限制,SMC-Net模型依然展示了出色的准确性和优良的泛化能力。
☆17Updated last year
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