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Classification of migraine stages based on resting-state EEG power


Cao, Z-H and Ko, L-W and Lai, K-L and Huang, S-B and Wang, S-J and Lin, C-T, Classification of migraine stages based on resting-state EEG power, Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), 12-17 July 2015, Killarney, Ireland ISSN 2161-4393 (2015) [Refereed Conference Paper]


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Copyright 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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DOI: doi:10.1109/IJCNN.2015.7280582


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:EEG, migraine, pre-ictal, resting state, EEG power, classification
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z-H (Dr Zehong Cao)
ID Code:132871
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2019-06-17
Downloads:29 View Download Statistics

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