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Exploring resting-state EEG complexity before migraine attacks


Cao, Z and Lai, K-L and Lin, C-T and Chuang, C-H and Chou, C-C and Wang, S-J, Exploring resting-state EEG complexity before migraine attacks, Cephalalgia, 38, (7) pp. 1296-1306. ISSN 0333-1024 (2018) [Refereed Article]

Copyright Statement

Copyright 2017 International Headache Society

DOI: doi:10.1177/0333102417733953


Objective: Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases.

Methods: Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack.

Results: The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 (p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76  4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity.

Conclusion: Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.

Item Details

Item Type:Refereed Article
Keywords:EEG, migraine, resting-state, complexity, 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 (Dr Zehong Cao)
ID Code:131542
Year Published:2018 (online first 2017)
Web of Science® Times Cited:28
Deposited By:Information and Communication Technology
Deposited On:2019-03-21
Last Modified:2019-04-10

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