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Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

机译：将非线性措施应用于脑节律：癫痫诊断的有效方法

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Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.
机译：癫痫是一种神经疾病，近5000万人一直在痛苦。这些统计数据表明癫痫诊断的重要性。脑电图（EEG）信号分析是癫痫表征最常见的方法之一;因此，施用各种策略来分类癫痫脑电图。在本文中，从EEG及其频率子带中提取了四种不同的非线性特征，例如包括HIGUCHI方法（HFD）和KATZ方法（KATZ方法（KATZ方法），HURST指数和L-Z复杂度测量。通过实现Relieff算法以后排名该特征。排序的特征是顺序施加到三种不同的分类器（MLPNN，线性SVM和RBF SVM）。根据用于本研究的数据集，存在名为ABCD / E，AB / CD / E，A / D / E，A / E和D / E的五个分类问题。在所有情况下，MLPNN是最准确的分类器。其提到的分类问题的性能分别为99.91％，98.19％，98.5％，100％和99.84％。结果表明，KFD是排名最高的特征;此外，Beta和Theta子带是最重要的频段，因为对于所有情况，顶部特征是从β和θ子带中提取的KFD。此外，仅通过使用这两个特征来获得高水平的精度，这两个特征降低了分类的复杂性。

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