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
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