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Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

机译：基于内在模式函数的相空间表示的脑电信号中癫痫发作的分类

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摘要

Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.
机译：癫痫病发作是人脑最常见的疾病，通常可从脑电图（EEG）信号中检测到。在本文中，我们提出了基于相空间表示（PSR）的癫痫性癫痫发作和无癫痫性脑电信号分类的新功能。首先使用经验模式分解（EMD）分解EEG信号，并为获得的固有模式函数（IMF）重建相空间。为了对癫痫性癫痫发作和无癫痫性脑电信号进行分类，已使用了二维（2D）和三维（3D）PSR。已经提出了基于IMF的2D和3D PSR的新功能，用于癫痫性癫痫发作和无癫痫性脑电信号的分类。已经定义了两种度量，即2D PSR的95％置信椭圆面积和EEG信号IMF的3D PSR的欧几里得距离的四分位数范围（IQR）。这些测得的参数显示出癫痫性癫痫发作和无癫痫性脑电信号之间存在显着差异。这些用于不同IMF的测量参数的组合已用于形成用于癫痫性癫痫性脑电信号分类的功能集。最小二乘支持向量机（LS-SVM）已被用于癫痫发作和无癫痫性脑电信号的分类，其分类性能已使用不同的核，即径向基函数（RBF），墨西哥帽小波和Morlet小波进行了评估内核。包括具有分类器各种性能参数的仿真结果，以显示所提出的方法对癫痫发作和无癫痫性脑电信号进行分类的有效性。

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