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Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification.
Biomedical Engineering / Biomedizinische Technik ( IF 1.7 ) Pub Date : 2020-04-28 , DOI: 10.1515/bmt-2018-0246
Dib Nabil 1 , Radhwane Benali 1 , Fethi Bereksi Reguig 1
Affiliation  

Epileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database.

中文翻译:

利用基于支持向量机分类的非线性和统计特征的EEG小波分解对癫痫发作进行识别。

癫痫性发作(ES)是神经性脑功能障碍。可以使用脑电图(EEG)信号检测ES。但是,使用长时间的EEG记录对ES进行目视检查是一个困难,耗时且昂贵的过程。因此,自动癫痫识别至关重要。本文提出了一种利用短时脑电图记录自动识别ES的新方法。该方法基于首先使用离散小波变换将EEG信号分解为子信号。然后,从获得的子信号中,确定不同的非线性参数,例如近似熵(ApEn),最大李雅普诺夫指数(LLE)和统计参数。这些参数以及通过高阶频谱分析计算出的相位熵,用作ES识别的多类支持向量机(MSVM)的输入向量。使用由德国波恩大学癫痫学系开发的标准EEG数据库评估提出的方法。通过大量的分类实验进行评估。计算不同的统计指标,即敏感性(Se),特异性(Sp)和分类准确性(Ac),并将其与科学研究文献中获得的统计指标进行比较。所得结果表明,所提出的方法具有较高的准确性,与使用相同的EEG数据库研究的最佳现有最新技术一样好。通过大量的分类实验进行评估。计算不同的统计指标,即敏感性(Se),特异性(Sp)和分类准确性(Ac),并将其与科学研究文献中获得的统计指标进行比较。所得结果表明,所提出的方法具有较高的准确性,与使用相同的EEG数据库研究的最佳现有最新技术一样好。通过大量的分类实验进行评估。计算不同的统计指标,即敏感性(Se),特异性(Sp)和分类准确性(Ac),并将其与科学研究文献中获得的统计指标进行比较。所得结果表明,所提出的方法具有较高的准确性,与使用相同的EEG数据库研究的最佳现有最新技术一样好。
更新日期:2020-04-28
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