title: [EEG signal classification based on EMD and SVM].
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.
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan 250100, China.
authors.forename: Shufang
authors.initials: S
authors.lastname: Li
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.forename: Dongmei
authors.initials: D
authors.lastname: Cai
authors.forename: Kai
authors.initials: K
authors.lastname: Liu
authors.forename: Jianlin
authors.initials: J
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D002965:Classification; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine