摘要
针对基于深度学习的脉搏波分类依赖大量标记数据,临床数据有限、标注成本高影响了脉搏波分类识别效果,设计一种基于卷积自编码器网络(CAE-Net)的脉搏波分类模型。首先,利用卷积神经网络(CNN)的局部特征提取能力和自编码器(AE)的压缩重构及降维特性构建卷积自编码器(CAE),结合脉搏波波形特点,在平均绝对误差损失函数中引入脉搏波时域特征约束,提升CAE对脉搏波低维特征自学习能力;其次,重用预训练CAE编码层网络和权重,构建CAE-Net,并利用有标记脉搏波数据进行网络微调。在心血管疾病数据集上的实验结果表明,CAE-Net的分类准确率为98.00%,F1值达到94.40%,相较于其他脉搏波分类模型,设计网络所提取特征的区分度较高,同时减弱了对标注脉搏波数据的依赖,在小样本脉搏波数据上具有较好分类效果。
Classification of pulse wave based on deep learning relies on a large number of labeled data,however,limited clinical data and expensive labeling costs hinder the pulse wave classification and recognition.A pulse wave classification model based on convolutional autoencoder networks(CAE-Net)is designed in this paper.Firstly,the convolutional autoencoder(CAE)is constructed,which combines the local feature extraction ability of convolutional neural network(CNN)and the compression reconstruction and dimension reduction characteristics of autoencoder(AE).And considering the characteristics of pulse wave,the time domain feature constraint of pulse wave is introduced into the mean absolute error loss function to improve the self-learning ability of CAE for low dimensional features.Secondly,the CAE-Net is constructed by reusing the coding layer network and weights of the pre-training CAE,then the network is fine tuned by using labeled pulse waves.Experiments on cardiovascular disease dataset show that the classification accuracy of CAE-Net is 98.00%,and the F 1 score is 94.40%.Compared with other classification models,the designed network can extract features with high discrimination,reduce the dependence on the labeled pulse waves,and perform well in the classification of small sample pulse wave data.
作者
逯鹏
王汉章
毛晓波
赵宇平
刘超
尚莉伽
孙智霞
LU Peng;WANG Hanzhang;MAO Xiaobo;ZHAO Yuping;LIU Chao;SHANG Lijia;SUN Zhixia(Institute of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China;Research Center for Intelligent Science and Engineering Technology of TCM,Zhengzhou 450001,China;China Academy of Chinese Medical Sciences,Beijng 100020,China;Primary and Secondary School Health Care in Beijing Dongcheng District,Beijing 100007,China;The Fifth Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2021年第5期56-61,共6页
Journal of Zhengzhou University（Engineering Science）
基金
国家重点研发计划项目(2020YFC2006100)
中央本级重大增减支项目(2060302)
河南省引智项目(河南省杰出外籍科学家工作室)(GZS2019008)。
关键词
心血管疾病
脉搏波
卷积神经网络
自编码器
cardiovascular disease
pulse wave
convolutional neural network
autoencoder
