计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 292-297.doi: 10.11896/jsjkx.190500181
所属专题: 医学图像
樊敏1, 王晓锋1, 孟小峰2
FAN Min1, WANG Xiao-feng1, MENG Xiao-feng2
摘要: 目前,心血管疾病已成为全球人类非传染性死亡的主要原因,死亡人数约占全球死亡总人数的1/3,且患病人数逐年增加。可穿戴设备被用于对心电图进行自动分类,以实现对心血管疾病的早监测、早预防。随着边缘机器学习和联邦学习的兴起,小型机器学习模型成为了人们关注的热点。针对可穿戴心电图设备低配置、低功耗及个性化的特点,文中研究了一种基于LSTM的轻量级网络结构,并采用自适应算法来优化病人个体的心电图分类模型。该模型利用MIT-BIH公开数据集开展实验,将VEB和SVEB的分类效果与其他相关研究进行了比较。实验结果表明,所提算法的模型结构简单且分类识别率高,能够满足可穿戴设备对病人心电图监测的需求。
中图分类号:
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