Electroencephalogram(EEG) analysis has important reference value in the diagnosis of epilepsy. The automatic classification of epileptic EEG can be used to judge the patient's situation in time,which is of great significance in clinical application. In order to solve the problem that the recognition accuracy is not high by using the single feature of EEG signals and avoid the influence of wavelet basis function selection on recognition results,a method of automatic discrimination of epileptic EEG signals based on S transform and permutation entropy is proposed. Firstly, the original signals are decomposed by discrete S transform, and then we calculate the fluctuation index of coefficients of each rhythm and combine the permutation entropy of EEG signals into a feature vector and use Real AdaBoost classifier to discriminate the epileptic EEG signals in muti-period. In this study, we used the epilepsy database from University of Bonn. Three groups of EEG signals, including the data of normal people with their eyes open, the data collected inside of the epileptic foci from patients during their interictal period and the data during their ictal period, were used to test effectiveness. The results of this study showed that the fluctuation index of each rhythm could be used to characterize the normal, interictal and ictal epileptic EEG signals effectively, and the recognition accuracy of multiple features was much higher than that of single feature. The average recognition accuracy could reach 98.13%. Compared with time-frequency feature extraction method or nonlinear feature extraction method only,the recognition accuracy was increased by more than 1.2% and 8.1% respectively, which was superior to the methods recorded in many other literatures. Therefore, this method has a good application prospect in diagnosis of epilepsy.
脑电图（EEG）分析对癫痫疾病的诊断具有重要的参考价值，对癫痫脑电信号的自动分类可以及时对患者的情况作出判断，在临床上有很重要的意义。为解决脑电信号采用单一特征识别率不高的问题，同时也为避免小波基函数的选取对分类结果的影响，本文提出了一种基于 S 变换和排列熵（PE）的癫痫脑电信号自动判别方法，首先将原始脑电信号进行离散 S 变换，再对变换后脑电信号各节律的系数分别求其波动指数，并与脑电信号的排列熵值共同组成特征向量送入 Real AdaBoost 分类器进行癫痫各时期的判别。本研究采用德国波恩大学癫痫研究中心数据库，对正常人清醒睁眼，癫痫患者发病间歇期致痫灶内及发作期 3 组脑电信号数据进行方法有效性检验。研究结果表明，各节律的波动指数可有效表征正常、癫痫发作间期和癫痫发作期脑电信号，且多种特征的识别率明显优于单一特征，平均识别率可达到 98.13%，相比于仅提取时频特征或非线性特征，识别率分别提高了 1.2% 和 8.1% 以上，优于文献中报道的多种方法。因此，本方法在癫痫疾病的诊断方面有较好的应用前景。.
Keywords: S transform; electroencephalogram; epilepsy; permutation entropy.