Title :
Classification of migraine stages based on resting-state EEG power
Author :
Ze-Hong Cao;Li-Wei Ko;Kuan-Lin Lai;Song-Bo Huang;Shuu-Jiun Wang;Chin-Teng Lin
Author_Institution :
Institute of Electrical Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan
fDate :
7/1/2015 12:00:00 AM
Abstract :
Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs´ resting-state EEG power. We collect migraineurs´ O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.σ
Keywords :
"Electroencephalography","Bismuth","Brain modeling"
Conference_Titel :
Neural Networks (IJCNN), 2015 International Joint Conference on
Electronic_ISBN :
2161-4407
DOI :
10.1109/IJCNN.2015.7280582