Comparing nonlinear features extracted in EEMD for discriminating focal and non-focal EEG signals

S. Ghofrani; H. Akbari

doi:10.1117/12.2523445

17 April 2019 Comparing nonlinear features extracted in EEMD for discriminating focal and non-focal EEG signals

S. Ghofrani, H. Akbari

Proceedings Volume 11071, Tenth International Conference on Signal Processing Systems; 1107106 (2019) https://doi.org/10.1117/12.2523445
Event: Tenth International Conference on Signal Processing Systems, 2018, Singapore, Singapore

Abstract

Removing the brain part, as the epilepsy source attack, is a surgery solution for those patients who have drug resistant. So, the epilepsy localization area is an essential step before surgery. The Electroencephalogram (EEG) signals of these areas are different and called as focal (F) where the EEG signals of other normal areas are known as non-focal (NF). Visual inspection of multi channels for detecting the F EEG signal is time consuming and along with human error. In this paper, we propose a new method based on ensemble empirical mode decomposition (EEMD) in order to distinguish the F and NF signals. For this purpose, EEG signal is decomposed by EEMD and the corresponding intrinsic mode functions (IMFs) are obtained. Then various nonlinear features including log energy (LE) entropy, Stein's unbiased risk estimate (SURE) entropy, information potential (IP) and centered correntropy (CC), are extracted. At the end, the input signal is classified as either F or NF by using support vector machine (SVM). Using nonlinear features, we achieved 89% accuracy in classification with tenfold cross validation strategy.

Citation Download Citation

S. Ghofrani and H. Akbari "Comparing nonlinear features extracted in EEMD for discriminating focal and non-focal EEG signals ", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 1107106 (17 April 2019); https://doi.org/10.1117/12.2523445

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