標題: Classification of Migraine Stages based on Resting-State EEG Power
作者: Cao, Ze-Hong
Ko, Li-Wei
Lai, Kuan-Lin
Huang, Song-Bo
Wang, Shuu-Jiun
Lin, Chin-Teng
生物資訊及系統生物研究所
電控工程研究所
腦科學研究中心
Institude of Bioinformatics and Systems Biology
Institute of Electrical and Control Engineering
Brain Research Center
關鍵字: Migraine;Pre-ictal;Resting State;EEG Power;Classification
公開日期: 2015
摘要: 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\' 01 and 02 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.
URI: http://hdl.handle.net/11536/134612
ISBN: 978-1-4799-1959-8
ISSN: 2161-4393
期刊: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
Appears in Collections:Conferences Paper