Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
作者：
Highlights：
• We propose new features for classification of epileptic seizure EEG signals.
• Features were extracted from PSR of IMFs of EEG signals.
• We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.
• LS-SVM classifier has been used for classification with the proposed features.
• Results were compared with other existing methods studied on the same EEG dataset.
摘要
•We propose new features for classification of epileptic seizure EEG signals.•Features were extracted from PSR of IMFs of EEG signals.•We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.•LS-SVM classifier has been used for classification with the proposed features.•Results were compared with other existing methods studied on the same EEG dataset.
论文关键词：Epilepsy,Electroencephalogram signal,Empirical mode decomposition,Phase space representation,Least squares support vector machine,Epileptic seizure classification
论文评审过程：Available online 8 September 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.030
