Pulse Wave Classification Model Based on Convolutional Autoencoder

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Pulse Wave Classification Model Based on Convolutional Autoencoder

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[1]LU Peng,WANG Hanzhang,MAO Xiaobo,et al.Pulse Wave Classification Model Based on Convolutional Autoencoder[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):56-61.[doi:10.13705/j.issn.1671-6833.2021.05.004]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 05 Page number: 56-61 Column: Public date: 2021-09-10

Title:: Pulse Wave Classification Model Based on Convolutional Autoencoder

Author(s):: LU Peng1; 2; WANG Hanzhang1; 2; MAO Xiaobo1; 2; ZHAO Yuping2; 3; LIU Chao1; 2; SHANG Lijia4; SUN Zhixia5; 1.Institute of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.Research Center for Intelligent Science and Engineering Technology of TCM, Zhengzhou 450001, China; 3.China Academy of Chinese Medical Sciences, Beijng 100020, China; 4.Primary and Secondary School Health Care in Beijing Dongcheng District, Beijing 100007, China; 5.The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Keywords:: cardiovascular disease; pulse wave; convolutional neural network; autoencoder

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.05.004

Abstract:: 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 F1 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.

References:: [1] LIU W C,FANG X,CHEN Q Q,et al.Reliability analysis of an integrated device of ECG, PPG and pressure pulse wave for cardiovascular disease[J].Microelectronics reliability,2018,87:183-187.
[2] IQBAL U,YING WAH T,HABIB UR REHMAN M,et al.Usage of model driven environment for the classification of ECG features:a systematic review[J].IEEE access,2018,6:23120-23136.

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Last Update: 2021-10-11