计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 186-193.doi: 10.11896/jsjkx.220200002
赵人行1, 徐频捷2,3, 刘瑶2
ZHAO Ren-xing1, XU Pin-jie2,3, LIU Yao2
摘要: 在智能诊断需求日益增长的背景下,提出了一种基于残差网络形式构建的卷积神经网络,该模型作为心电信号房颤分类的方法,使用MIT-BIH的心房颤动公开数据集来验证所提方法的效果,以辅助房颤自动检测。针对心电信号二分类问题,首先,对数据集进行前期数据预处理,然后将处理后的数据输入到卷积神经网络,以构建深度学习模型,使其对房颤特征进行自动提取,最后利用深度学习模型进行房颤检测,通过五折交叉验证得到构建模型分类的敏感性为99.26%,特异性为99.42%,阳性预测值为99.61%,准确率为99.47%。将所提模型的性能与已有模型进行了比较,证实了所提模型用于房颤检测的可行性。由此得出结论,通过残差网络构建的房颤自动检测系统可以达到房颤的良好分类效果,有助于房颤自动检测。
中图分类号:
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