基于GoogLeNet和ResNet的深度融合神经网络在脉搏波识别中的应用
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

中国科学院苏州纳米技术与纳米仿生研究所资助项目(Y6AAJ11001)


Pulse Wave Recognition Using Deep Hybrid Neural Networks Based on GoogLeNet and ResNet
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了提高脉搏波识别的准确率,提出改进的深度融合神经网络MIRNet2.首先,经过主波提取、划分周期和制作hdf5数据集等,获得Caffe可处理的数据集.其次,提出由Inception模块和残差模块构成的融合网络Inception-ResNet (IRNet),包含IRNet1、IRNet2和IRNet3.在此基础上,改进Inception模块、残差模块和池化模块,构造Modified Inception-ResNet (MIRNet),包含MIRNet1和MIRNet2.与本文其它神经网络相比,MIRNet2的分类性能最好,特异性、灵敏度和准确率分别达到87.85%、88.05%和87.84%,参数量和运算量也少于IRNet3.

    Abstract:

    To improve the accuracy of pulse wave recognition, MIRNet2 is proposed, which is a kind of modified deep hybrid neural networks. Firstly, processable data sets of Caffe are obtained by main pulse extraction, segmenting cycle and making hdf5 data sets. Secondly, deep hybrid neural networks are designed. Inception-ResNet (IRNet) is consisted of inception modules and residual modules, containing IRNet1, IRNet2 and IRNet3. Subsequently, Modified Inception-ResNet (MIRNet) composed of modified Inception modules, residual modules and pooling modules (or reduction modules) is proposed, including MIRNet1 and MIRNet2. Compared with other neural networks in the study, MIRNet2 is the best one, with the specificity of 87.85%, the sensitivity of 88.05% and the accuracy of 87.84%, respectively. In addition, parameters and operations of MIRNet2 are also less than that of IRNet3.

    参考文献
    相似文献
    引证文献
引用本文

张选,胡晓娟.基于GoogLeNet和ResNet的深度融合神经网络在脉搏波识别中的应用.计算机系统应用,2019,28(10):15-26

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-03-20
  • 最后修改日期:2019-04-17
  • 录用日期:
  • 在线发布日期: 2019-10-15
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号