EEG signal classification based on EMD and SVM.
- Author:
Shufang LI
1
;
Weidong ZHOU
;
Dongmei CAI
;
Kai LIU
;
Jianlin ZHAO
Author Information
1. School of Information Science and Engineering, Shandong University, Jinan 250100, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Classification;
Electroencephalography;
methods;
Epilepsy;
physiopathology;
Humans;
Pattern Recognition, Automated;
methods;
Signal Processing, Computer-Assisted;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2011;28(5):891-894
- CountryChina
- Language:Chinese
-
Abstract:
The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.
