基於穩態視覺誘發電位開發偵測偏頭痛發作之系統

Abstract

偏頭痛是一種常見的神經性疾病，伴隨著反覆發作頭痛的特性。偏頭痛病患容易因為外界刺激的影響使腦部處於興奮性較高的狀態，可能因為腦部的興奮性超過一個臨界點，而造成偏頭痛發作。過去研究指出習慣化指標會隨著偏頭痛週期變化，並用於解釋病患生理狀態的關鍵症狀。因此，此篇研究透過穩態視覺誘發電位實驗收集腦波資料，計算習慣化指標。於偏頭痛周期中，使用習慣化指標辨識病患的生理狀態，並以此指標建立偵測偏頭痛狀態的分類系統。此分類系統可應用於偵測偏頭痛發作，並作為臨床上個人化的頭痛監控程式與早期偏頭痛的偵測。

Migraine is a common neurological disease, which characterized by recurrent attacks triggered by the migraineurs’ brain lacking for habituation, due to the stimulations from the outside world that increase the excitability of brain activity, which have been considered as the possible reasons for migraine seizure. The variation of habituation level within the migraine cycle is proposed to be a critical symptom to describe the physiological states of migraine headache. This study proposed Steady-State Visual Evoked Potentials (SSVEP) examination to utilize habituation for classifying the different physiologic states of migraine cycle, and implement a classification system to determine different migraine stages. The developed system may be extended to detect migraine seizure, and provide an opportunity to a clinically individual-based headache monitoring program, aiming for early migraine detection.