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Extraction of SSVEPs-based inherent fuzzy entropy using a wearable headband EEG in migraine patients


Cao, Z and Lin, C-T and Lai, K-L and Ko, L-W and King, J-T and Liao, L-W and Fuh, J-L and Wang, S-J, Extraction of SSVEPs-based inherent fuzzy entropy using a wearable headband EEG in migraine patients, IEEE Transactions on Fuzzy Systems, 28, (1) pp. 14-27. ISSN 1063-6706 (2020) [Refereed Article]


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DOI: doi:10.1109/TFUZZ.2019.2905823


Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the robustness of brain systems. In this study, we present a novel application of multi-scale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e. interictal (baseline) and pre-ictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2 and Fpz electrodes to collect EEG signals from 80 participants (40 migraine patients and 40 healthy controls [HCs]) under the following two conditions: during resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the inter-ictal phase but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p<0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the pre-ictal phase exhibited significantly lower values than patients in the inter-ictal phase (FDR-adjusted p<0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 6% and AUC of 0.87 for classifying inter-ictal and pre-ictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.

Item Details

Item Type:Refereed Article
Keywords:migraine, SSVEP, EEG, inherent fuzzy entropy
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:131573
Year Published:2020 (online first 2019)
Web of Science® Times Cited:67
Deposited By:Information and Communication Technology
Deposited On:2019-03-23
Last Modified:2020-09-10
Downloads:17 View Download Statistics

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