Yang Li, He Jiaxiu, Liu Ding, Zheng Wen, Song Zhi
Department of Epilepsy Centre and Neurology, The Third Xiangya Hospital, Central South University, Changsha 410000, China.
Brain Sci. 2022 Dec 17;12(12):1731. doi: 10.3390/brainsci12121731.
Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel-Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.
癫痫是最严重的神经系统疾病之一；可通过视频脑电图准确诊断。在本研究中，我们分析了微状态癫痫脑电图（EEG），以辅助癫痫的诊断和识别。我们从湘雅三医院招募了局灶性癫痫患者和健康参与者，并记录了他们的静息EEG数据。在本研究中，通过微状态分析对EEG数据进行分析，并基于EEG微状态序列的特征，包括微状态参数（持续时间、发生率和覆盖率）、线性特征（中位数、第二四分位数、均值、峰度和偏度）和非线性特征（佩特罗西安分形维数、近似熵、样本熵、模糊熵和莱姆尔-齐夫复杂度），使用支持向量机（SVM）分类器对癫痫EEG进行自动分类。在伽马子频段，以微状态参数作为模型对发作间期癫痫的识别效果最佳，准确率为87.18%，召回率为70.59%，曲线下面积为94.52%。通过从EEG微状态中提取的特征对发作间期癫痫有识别效果，在4~45Hz频段内有所不同，准确率为79.55%。基于SVM分类器，微状态参数和EEG特征可有效用于癫痫EEG的分类，与EEG特征相比，微状态参数能更好地对癫痫EEG进行分类。
