Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.
作者： Zhixian Yang , Yinghua Wang , Gaoxiang Ouyang
DOI: 10.1155/2014/140863
关键词:
摘要: Background electroencephalography (EEG), recorded with scalp electrodes, in children electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed aged 3–9 years. The individuals had the same characteristics of ones but presented a normal EEG. Recordings were undertaken awake relaxed states their eyes open. complexity background EEG was evaluated using permutation entropy (PE) sample (SampEn) combination ANOVA test. It can be seen that measures are significantly different between patients subjects. Then, classification framework based on adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish signals. results promising accuracy about 89% achieved.
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