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。
☆18Jun 1, 2024Updated last year
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