Xiang Jie, Li Conggai, Li Haifang, Cao Rui, Wang Bin, Han Xiaohong, Chen Junjie
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China; The International WIC Institute, Beijing University of Technology, Beijing 100022, People's Republic of China; Graduate School of Natural Science and Technology, Okayama University, 700-8530 Okayama, Japan.
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, People's Republic of China.
J Neurosci Methods. 2015 Mar 30;243:18-25. doi: 10.1016/j.jneumeth.2015.01.015. Epub 2015 Jan 19.
Entropy is a nonlinear index that can reflect the degree of chaos within a system. It is often used to analyze epileptic electroencephalograms (EEG) to detect whether there is an epileptic attack. Much research into the state inspection of epileptic seizures has been conducted based on sample entropy (SampEn). However, the study of epileptic seizures based on fuzzy entropy (FuzzyEn) has lagged behind.
We propose a method of state inspection of epileptic seizures based on FuzzyEn. The method first calculates the FuzzyEn of EEG signals from different epileptic states, and then feature selection is conducted to obtain classification features. Finally, we use the acquired classification features and a grid optimization method to train support vector machines (SVM).
The results of two open-EEG datasets in epileptics show that there are major differences between seizure attacks and non-seizure attacks, such that FuzzyEn can be used to detect epilepsy, and our method obtains better classification performance (accuracy, sensitivity and specificity of classification of the CHB-MIT are 98.31%, 98.27% and 98.36%, and of the Bonn are 100%, 100%, 100%, respectively).
COMPARISONS WITH EXISTING METHOD(S): To verify the performance of the proposed method, a comparison of the classification performance for epileptic seizures using FuzzyEn and SampEn is conducted. Our method obtains better classification performance, which is superior to the SampEn-based methods currently in use.
The results indicate that FuzzyEn is a better index for detecting epileptic seizures effectively. The FuzzyEn-based method is preferable, exhibiting potential desirable applications for medical treatment.
熵是一种非线性指标，能够反映系统内的混乱程度。它常被用于分析癫痫脑电图（EEG），以检测是否存在癫痫发作。基于样本熵（SampEn），已经开展了许多关于癫痫发作状态检测的研究。然而，基于模糊熵（FuzzyEn）的癫痫发作研究却相对滞后。
我们提出了一种基于模糊熵的癫痫发作状态检测方法。该方法首先计算不同癫痫状态下脑电信号的模糊熵，然后进行特征选择以获得分类特征。最后，我们使用获取的分类特征和网格优化方法来训练支持向量机（SVM）。
癫痫患者两个公开脑电数据集的结果表明，癫痫发作与非癫痫发作之间存在显著差异，使得模糊熵可用于检测癫痫，并且我们的方法获得了更好的分类性能（CHB - MIT数据集分类的准确率、灵敏度和特异性分别为98.31%、98.27%和98.36%，波恩数据集的分别为100%、100%、100%）。
为了验证所提方法的性能，对使用模糊熵和样本熵的癫痫发作分类性能进行了比较。我们的方法获得了更好的分类性能，优于目前使用的基于样本熵的方法。
结果表明，模糊熵是有效检测癫痫发作的更好指标。基于模糊熵的方法更可取，在医学治疗方面展现出潜在的理想应用。
