Anuragi Arti, Sisodia Dilip Singh, Pachori Ram Bilas
Department of Computer Science & Engineering, National Institute of Technology Raipur, G E Road Raipur, Chhattisgarh 492010, India.
Department of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Indore, Madhya pradesh 453552, India.
Artif Intell Med. 2023 May;139:102542. doi: 10.1016/j.artmed.2023.102542. Epub 2023 Apr 5.
BACKGROUND/INTRODUCTION: Manual detection and localization of the brain's epileptogenic areas using electroencephalogram (EEG) signals is time-intensive and error-prone. An automated detection system is, thus, highly desirable for support in clinical diagnosis. A set of relevant and significant non-linear features plays a major role in developing a reliable, automated focal detection system.
A new feature extraction method is designed to classify focal EEG signals using eleven non-linear geometrical attributes derived from the Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) segmented rhythm's second-order difference plot (SODP). A total of 132 features (2 channels × 6 rhythms × 11 geometrical attributes) were computed. However, some of the obtained features might be non-significant and redundant features. Hence, to acquire an optimal set of relevant non-linear features, a new hybridization of 'Kruskal-Wallis statistical test (KWS)' with 'VlseKriterijuska Optimizacija I Komoromisno Resenje' termed as the KWS-VIKOR approach was adopted. The KWS-VIKOR has a two-fold operational feature. First, the significant features are selected using the KWS test with a p-value lesser than 0.05. Next, the multi-attribute decision-making (MADM) based VIKOR method ranks the selected features. Several classification methods further validate the efficacy of the features of the selected top n%.
The proposed framework has been evaluated using the Bern-Barcelona dataset. The highest classification accuracy of 98.7% was achieved using the top 35% ranked features in classifying the focal and non-focal EEG signals with the least-squares support vector machine (LS-SVM) classifier.
The achieved results exceeded those reported through other methods. Hence, the proposed framework will more effectively assist the clinician in localizing the epileptogenic areas.
背景/引言：利用脑电图（EEG）信号手动检测和定位大脑的致痫区域既耗时又容易出错。因此，非常需要一种自动化检测系统来辅助临床诊断。一组相关且重要的非线性特征在开发可靠的自动化局灶性检测系统中起着主要作用。
设计了一种新的特征提取方法，使用从基于傅里叶 - 贝塞尔级数展开的经验小波变换（FBSE - EWT）分割节律的二阶差分图（SODP）中导出的11个非线性几何属性来对焦性EEG信号进行分类。总共计算了132个特征（2个通道×6种节律×11个几何属性）。然而，一些获得的特征可能是无意义和冗余的特征。因此，为了获得一组最优的相关非线性特征，采用了一种新的“Kruskal - Wallis统计检验（KWS）”与“VlseKriterijuska Optimizacija I Komoromisno Resenje”的混合方法，称为KWS - VIKOR方法。KWS - VIKOR具有双重操作特征。首先，使用p值小于0.05的KWS检验选择显著特征。接下来，基于多属性决策（MADM）的VIKOR方法对所选特征进行排序。几种分类方法进一步验证了所选前n%特征的有效性。
使用伯尔尼 - 巴塞罗那数据集对所提出的框架进行了评估。使用前35%排名的特征，通过最小二乘支持向量机（LS - SVM）分类器对焦性和非焦性EEG信号进行分类时，实现了98.7%的最高分类准确率。
所取得的结果超过了通过其他方法报告的结果。因此，所提出的框架将更有效地帮助临床医生定位致痫区域。
