Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

作者：Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang^*

来源：The Scientific World Journal, 2014, 2014: 140863.

DOI：10.1155/2014/140863

• ELECTRICAL STATUS EPILEPTICUS
• ABSENCE EPILEPSY PATIENTS
• TIME-SERIES ANALYSIS
• PERMUTATION ENTROPY
• APPROXIMATE ENTROPY
• SAMPLE ENTROPY
• SLOW SLEEP
• ELECTROENCEPHALOGRAM
• ENCEPHALOPATHY
• REGULARITY

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.

• 出版日期2014
• 单位北京大学; 北京师范大学