基于跨层全连接神经网络的癫痫发作期识别  

Epileptic EEG identification with cross layer fully connected neural network

作　　者：王凤琴 卢官明[2] 柯亨进 肖新凤 Wang Fengqin;Lu Guanming;Ke Hengjin;Xiao Xinfeng(College of Physics & Electronic Science,Hubei Normal University,Huangshi Hubei 435102,China;College of Telecommunications & Information Engineering,Nanjing University of Posts & Telecommunications,Nanjing 210003,China;School of Computer Science,Wuhan University,Wuhan 430072,China;Guangdong Polytechnic of Environmental Protection Engineering,Guangzhou 528216,China)

机构地区：[1]湖北师范大学物理与电子科学学院,湖北黄石435102 [2]南京邮电大学信息与通信工程学院,南京210003 [3]武汉大学计算机学院,武汉430072 [4]广东环境保护工程职业学院,广州528216

出　　处：《计算机应用研究》2019年第7期2098-2103,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61071167,61501249)

摘　　要：在缺乏足够先验知识下,自适应癫痫发作期识别异常困难。提出一种新的度量通道之间的同步特征计算方法(聚类划分互信息),以相关矩阵方式组织单窗口内全局同步特征模式,进而设计一种跨层全连接神经网络分类器,对非平稳同步特征模式实现自适应分类。实验表明该方法可获得[98.19%±0.24%]精确度,[98.27%±0.51%]敏感度和[98.11%±0.36%]特异度,超过了现有大部分方法的分类性能。另外,该方法无须去噪和去伪迹等预处理过程;而且其仅需设置一个超参数(时间窗),避免了过多的潜在错误参数设置而导致的分类性能的降低。Under the circumstance of insufficient prior knowledge,it becomes even more important to adaptively classify the synchronization dynamics to accurately characterize the intrinsic nature of seizure activities represented by the EEG.This paper first measured the global synchronization by calculating clustering partition mutual information(MI) of all EEG data channels.Then it designed a cross layer fully connected net to adaptively characterize the synchronization dynamics captured correlation matrices and automatically identify the seizure states of the EEG.It also performed experiments over the CHB-MIT scalp EEG dataset to evaluate the proposed approach.It identified seizure states with an accuracy,sensitivity and specificity of [98.19%± 0.24%],[98.27%±0.51%],and[98.11%±0.36%],respectively.The resulted performance was superior to those of most existing methods over the same dataset.The approach alleviated the need for strictly denoising and artifact removing based on the EEG prior knowledge that is mandatory for existing methods.Only one hyper-parameter need be set manually to avoid getting worse performance because of complex parameter setting.

关 键 词：聚类划分互信息 脑电 癫痫 同步 模式分类 跨层全连接神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]