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Classification of migraine stages based on resting-state EEG power

Citation

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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Official URL: https://ieeexplore.ieee.org/abstract/document/7280...

DOI: doi:10.1109/IJCNN.2015.7280582

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.

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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