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基于改进径向基函数的癫痫脑电信号分类算法

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF.

作者信息

Zhou Dongmei, Li Xuemei

机构信息

College of Information Science and Technology, Chengdu University of Technology, Chengdu, China.

Xijing Hospital, Air Force Medical University, Xi'an, China.

出版信息

Front Neurosci. 2020 Jun 23;14:606. doi: 10.3389/fnins.2020.00606. eCollection 2020.

Abstract

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.

摘要

癫痫是一种慢性复发性短暂性脑功能障碍综合征。它的特征是由脑神经元异常放电引起的反复发作性癫痫。癫痫是神经系统的常见疾病之一。脑电图(EEG)信号分析是当前研究的热点话题。为了准确解决癫痫脑电信号分类问题,我们对癫痫脑电信号进行了深入研究,从线性和非线性角度分析特征,将其输入改进的径向基函数(RBF)模型以动态提取有效特征,并引入一对一策略分类器以降低错误分类的概率。实验表明,所提出的算法具有很强的鲁棒性和较高的癫痫信号识别率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a683/7324866/aed421d184cc/fnins-14-00606-g0001.jpg

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