计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 121-126.doi: 10.11896/jsjkx.200700103
杨春德1,2, 贾竹1, 李欣蔚2
YANG Chun-de1,2, JIA Zhu1, LI Xin-wei2
摘要： 探索高效、快速、精准的心电信号识别分类算法是心电诊断的难点。基于心电片段的识别分类更贴合临床应用。基于此,文中将改进的深度卷积生成对抗网络(Deep Convolutional Generative Adversarial Networks,DCGAN)用于数据扩充,将优化的一维U-Net++用于心律不齐的片段信号识别。连续截取MIT-BIH数据库中1 200个采样点的心电片段作为实验数据集,以每条片段记录中心拍标签次数出现最多的类型作为整段记录的标签。再将优化的一维U-Net++作为DCGAN结构的生成器实现部分数据扩充,以解决数据不平衡的问题。在原始心电信号未经过任何预处理以及生成的扩充数据用于完成小波阈值去噪的情况下,优化的一维U-Net++模型对于正常、室性早搏、左束支阻滞、右束支阻滞4类不同的心电类型训练集的准确率能够达到98.10%,且对于测试集的精准率、召回率和F1值等指标均有较好的结果。在相同实验数据集下,优化的一维U-Net++模型比U-Net模型的准确率提高了1.05%;在相同实验参数的条件下,与欠采样数据对比,经DCGAN数据扩充后的数据集实验模型的准确率提高了0.85%。
中图分类号:
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