AdaBoost ensemble learning using the transitional variance of EEG entropy during repetitive visual stimulation classified inter-ictal and pre-ictal migraine phases with 81% accuracy and an AUC of 0.87.
Observational (n=80)
No
Does SSVEP-based inherent fuzzy entropy using a wearable EEG differentiate between inter-ictal and pre-ictal phases in migraine patients?
Inherent fuzzy entropy using repetitive SSVEPs can accurately classify inter-ictal and pre-ictal migraine phases, offering a potential pre-ictal alert system.
Effect estimate: Accuracy 81%
p-value: p=<0.05
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 multiscale 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 preictal (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 interictal 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 (FDRadjustedp <; 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjustedp <; 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 ± 6% and area under the curve of 0.87 for classifying interictal and preictal 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.
Cao et al. (2019) conducted an observational in Migraine without aura (n=80). Repetitive steady-state visual evoked potentials (SSVEPs) vs. Healthy controls and inter-ictal phase was evaluated on Classification of inter-ictal and pre-ictal migraine phases using AdaBoost ensemble learning based on transitional variance of EEG entropy (Accuracy 81%, p=<0.05). AdaBoost ensemble learning using the transitional variance of EEG entropy during repetitive visual stimulation classified inter-ictal and pre-ictal migraine phases with 81% accuracy and an AUC of 0.87.