Krishnaprasanna R, Vijaya Baskar V, Panneerselvam John
Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India.
School of EEE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India.
Phys Eng Sci Med. 2021 Jun;44(2):545-556. doi: 10.1007/s13246-021-01006-1. Epub 2021 May 6.
Epilepsy is a neurological disorder that affects people of any age, which can be detected by Electroencephalogram (EEG) signals. This paper proposes a novel method called Volume of Phase Space Representation (VOPSR) to classify seizure and seizure-free EEG signals automatically. Primarily, the recorded EEG signal is disintegrated into several Intrinsic Mode Functions (IMFs) using the Empirical Mode Decomposition (EMD) method and the three-dimensional phase space have been reconstructed for the obtained IMFs. The volume is measured for the obtained 3D-PSR for different IMFs called VOPSR, which is used as a feature set for the classification of Epileptic seizure EEG signals. Support vector machine (SVM) is used as a classifier for the classification of epileptic and epileptic-free EEG signals. The classification performance of the proposed method is evaluated under different kernels such as Linear, Polynomial and Radial Basis Function (RBF) kernels. Finally, the proposed method outperforms noteworthy state-of-the-art classification methods in the context of epileptic EEG signals, achieving 99.13% accuracy (average) with the Linear, Polynomial, and RBF kernels. The proposed technique can be used to detect epilepsy from the EEG signals automatically without human intervention.
癫痫是一种影响任何年龄段人群的神经系统疾病，可通过脑电图（EEG）信号检测出来。本文提出了一种名为相空间表示体积（VOPSR）的新方法，用于自动分类癫痫发作和无癫痫发作的EEG信号。首先，使用经验模态分解（EMD）方法将记录的EEG信号分解为几个固有模态函数（IMF），并为获得的IMF重建三维相空间。对获得的不同IMF的三维相空间表示（3D-PSR）测量体积，即VOPSR，将其用作癫痫发作EEG信号分类的特征集。支持向量机（SVM）用作癫痫和无癫痫EEG信号分类的分类器。在不同核函数（如线性、多项式和径向基函数（RBF）核）下评估所提出方法的分类性能。最后，在所提出的方法在癫痫EEG信号的背景下优于值得注意的现有分类方法，使用线性、多项式和RBF核时平均准确率达到99.13%。所提出的技术可用于在无需人工干预的情况下从EEG信号中自动检测癫痫。
