Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.
Department of Pediatrics, Peking University First Hospital, No. 1 of Xian Men Street, Xicheng District, Beijing 100034, China.
Zhixian
Z
Yang
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
Yinghua
Y
Wang
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
Gaoxiang
G
Ouyang
D058256:Brain Waves; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography; D006801:Humans; D012890:Sleep; D013226:Status Epilepticus / Q000175:diagnosis*