EEG classification approach based on the extreme learning machine and wavelet transform.
Automatic detection and classification of electroencephalogram (EEG) epileptic activity aid diagnosis and relieve the heavy workload of doctors. This article presents a new EEG classification approach based on the extreme learning machine (ELM) and wavelet transform (WT). First, the WT is used to extract useful features when certain scales cover abnormal components of the EEG. Second, the ELM algorithm is used to train a single hidden layer of feedforward neural network (SLFN) features. Finally, the SLFN is tested with interictal and ictal EEGs. The experiments demonstrated that the proposed approach achieved a satisfactory classification rate of 99.25% for interictal and ictal EEGs.
School of Information Science and Engineering, Shandong University, Jinan, China.
Qi
Q
Yuan
Weidong
W
Zhou
Jing
J
Zhang
Shufang
S
Li
Dongmei
D
Cai
Yanjun
Y
Zeng
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification* / Q000503:physiopathology; D006801:Humans; D016571:Neural Networks, Computer; D058067:Wavelet Analysis