典型文献
EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss
文献摘要：
According to the World Health Organization,about 50 million people world-wide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98％and 99.05％,respectively,indicating the application value of the new model.
文献关键词：
中图分类号：
作者姓名：
YANG Li-jun;JIANG Shu-yue;WEI Xiao-ge;XIAO Yun-hai
作者机构：
School of Mathematics and Statistics,Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms,Henan University,Kaifeng 475004,China;Center for Applied Mathematics of Henan Province,Henan University,Zhengzhou 450046,China
文献出处：
引用格式：
[1]YANG Li-jun;JIANG Shu-yue;WEI Xiao-ge;XIAO Yun-hai-.EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss)[J].数学季刊（英文版）,2022(03):281-300
A类：
Bonn,Childrens,Hauz,Khas
B类：
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AB值：
0.59855
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