Epilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

中文翻译：

---

分析用于癫痫发作检测的固有模式函数 (IMF) 的统计系数和自回归参数。


癫痫是一种持续性神经系统疾病，影响着全世界超过 5000 万人。它的特点是反复发作，定义为可能涉及人体的短暂不自主运动。脑电图（EEG）信号通常用于检测癫痫发作。本文介绍了一种新的特征提取方法，用于对癫痫发作和无癫痫发作脑电图时间段进行分类。所提出的方法依赖于经验模态分解（EMD）、统计和自回归（AR）参数。 EMD 方法将 EEG 时间段分解为一组有限的本征模态函数 (IMF)，从中计算统计系数和自回归参数。然而，随着 IMF 数量的增加，计算出的特征可能具有高维度，因此采用学生 t 检验和 Mann-Whitney U 检验进行特征排序，以撤回较低显着性特征。通过应用前馈多层感知器神经网络（MLPNN）分类器，所获得的特征已用于对癫痫发作和无癫痫发作脑电图信号进行分类。在德国波恩大学提供的脑电图数据库上进行的实验结果证明了所提出方法的有效性，该方法通过分类精度（CA）评估的性能与文献中报告的其他现有性能进行了比较。