基于ELM和近似熵的脑电信号检测方法  

Approach of EEG detection based on ELM and approximate entropy

作　　者：袁琦[1] 周卫东[1] 李淑芳[1] 蔡冬梅[1] 

机构地区：[1]山东大学信息科学与工程学院,济南250100

出　　处：《仪器仪表学报》2012年第3期514-519,共6页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(30870666);山东省自然科学基金(Y2007G31);山东大学创新基金(2009JC004)资助项目

摘　　要：脑电癫痫波的自动检测与分类对癫痫病情的诊断具有重要意义。提出了一种基于极端学习机(extreme learning ma-chine,ELM)和近似熵的脑电信号检测方法。首先,计算脑电信号的近似熵作为非线性特征,并与利用小波变换技术提取的线性特征波动指数相结合,组成特征向量,然后将特征向量送入单隐层前馈神经网络,采用ELM学习算法训练网络。实验表明,与BP(backpropagation)和SVM(support vector machine)算法相比,ELM在训练时间和识别精度两方面性能最佳,对用于实验的脑电数据检测识别率达到98%以上。The automatic detection and classification of epileptic EEG are significant in diagnosis of epilepsy.This paper presents a new EEG detection approach based on extreme learning machine（ELM） and approximate entropy（ApEn）.Firstly,the value of the ApEn is calculated as the non-linear feature.Then wavelet transform is used to compute the fluctuation indices as linear features.Secondly,ELM algorithm is employed to train single-hidden layer feedforward neural network（SLFN） with the features.Finally,the SLFN is tested.Experiments demonstrate that compared with BP algorithm and support vector machine（SVM）,the performance of ELM is better in terms of training time and classification accuracy,and a satisfied classification rate more than 98% for interictal and ictal EEGs is achieved.

关 键 词：癫痫脑电 近似熵 极端学习机 反向传播算法 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] R318.04[自动化与计算机技术—计算机科学与技术]