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- Title
基于卷积自编码器网络的脉搏波分类模型.
- Authors
逮鹏; 王汉; 章毛; 晓波; 赵宇平; 尚莉伽; 孙智霞
- 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 feature s・ 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 Fl 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.
- Publication
Journal of Zhengzhou University: Engineering Science, 2021, Vol 42, Issue 5, p56
- ISSN
1671-6833
- Publication type
Article
- DOI
10.13705/j.issn.1671-6833.2021.05.004