Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients

Abstract

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. inter-ictal (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 pre-ictal phase. In the 1 st SSVEP, occipital EEG entropy of the HCs was significantly higher than that of patents in the pre-ictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1 st and 5 th 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 pre-ictal alert to migraine patients.

A New Divergence Measure of Pythagorean Fuzzy Sets Based on Belief Function and Its Application in Medical Diagnosis

“…With the development of fuzzy mathematics, medical diagnosis has addressed more and more attention from the research society of applied computer mathematics. In the diagnostic process of medical profession, improving the processing capacity of various uncertain and inconsistent information and achieving more accurate decision-making have become the major challenges in the development of medical diagnosis [1][2][3]. Up to now, the process of medical diagnosis is driven by various theoretical studies, such as fuzzy sets theory [4][5][6], intuitionistic fuzzy sets [7][8][9][10], interval-valued intuitionistic fuzzy sets [11][12][13], and quantum decision [14][15][16].…”

Entailment for intuitionistic fuzzy sets based on generalized belief structures

“…The real world is very complex with uncertainty 40–43 . Dealing with uncertainty is an open issue and many tools are presented to address this issue 44–47 . Many math models such as network analysis, 48–51 risk and reliability analysis, 52–54 visible graph, 55 and fuzzy sets 56–60 .…”

Tensor Decomposition for EEG Signals Retrieval

“…Given a time series recorded physiological data, all data samples were carried by a vector. The power spectrum analysis of the time series has often been applied for investigating physiological (e.g., EEG) oscillations by computational intelligence models [7][8][9][10][11][12][13][14] and associated healthcare applications [15][16][17][18][19][20]. Recently, multiple electrodes are often used to collect EEG data in the experiment.…”