Improving Convolutional Neural Network Using Accelerated Proximal Gradient Method for Epilepsy Diagnosis

被引:0
作者
Li, Dazi [1 ]
Wang, Guifang [1 ]
Song, Tianheng [1 ]
Jin, Qibing [1 ]
机构
[1] Beijing Univ Chem Technol, Dept Automat, Beijing, Peoples R China
来源
2016 UKACC 11TH INTERNATIONAL CONFERENCE ON CONTROL (CONTROL) | 2016年
关键词
epilepsy; convolutional neural network; proximal gradient; feature extraction; classification; EXTREME LEARNING-MACHINE; CLASSIFICATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The task of epilepsy diagnosing in medicine by classification of electroencephao-graph (EEG) signals is considered. Since an EEG signal has a large number of dimensions as an input sample vector, many previous classification methods have been proposed as hybrid frameworks, which are structurally complex and computationally expensive. In this paper, convolutional neural network (CNN) is used to realize feature extraction and classification simultaneously. The scheme of CNN is adopted to overcome the curse of dimensionality. Meanwhile, the accelerated proximal gradient method is used to increase the training ratio. Experimental results show that the proposed method achieves ideal accuracy of epilepsy diagnosis and converges faster than CNNs based on traditional gradient back propagation.
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页数:6
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