基於靜息狀態腦波之偏頭痛預測系統

Abstract

偏頭痛（Migraine）是一種具有復雜的病理和生理學特徵, 在一個月的時間內會復發性頭痛的神經系統疾病。目前只有一小群偏頭痛患者（13-31％）在頭痛發作之前經歷了常見的視覺先兆的短暫神經症狀，然而大多數患者不具有前兆症狀，屬於無先兆的偏頭痛（MO）。本研究欲以腦波（EEG）的頻譜能量（power spectral）、腦區一致性（coherence）和熵（entropy）變化提出神經生理證據，予以佐證在在靜息狀態（resting-state）下大腦皮質活動與無先兆偏頭痛狀態改變之關聯，並建立一套偏頭痛預測系統。首先，我們探討了的不同腦波儀器的特點，腦波訊號預處理和去除雜訊的方法，以及多種腦波訊號特徵提取技術，包括能量、一致性和熵分析。其次，在基於橫斷面的（cross-sectional）實驗中，我們探討了偏頭痛週期性變化過程中的腦波特徵。從頻譜能量和腦區連結度建立的分析顯示，相較於頭痛間期（inter-ictal）的病人，頭痛前期（pre-ictal）的病人EEG頻譜強度和不同腦區的一致性呈現顯著增強的趨勢。和控制組（healthy controls）比較，頭痛間期和頭痛前期分別呈現顯著減弱和“常態化”（normalization）的頻譜能量和腦區一致性強度。再次，在基於縱貫性（longitudinal）研究中，我們用可攜帶式腦波儀來評估偏頭痛前的腦波特徵變化。結果顯示在額葉前部（prefrontal）區，相較於頭痛間期和控制組，頭痛前期的EEG熵呈現顯著上升和“常態化”。最後，根據上述研究在神經科學上之發現，為監控個人偏頭痛發作前的狀態，本研究提出一新穎的基於支持向量機（SVM）演算法，用於偵測頭痛前期的準確度可以達到76%。其特點是使用單一（前額）區域之腦波變化以促成腦機介面（brain-computer interface），可以在未來的臨床試驗中得以應用。

Migraine is a common episodic neurological disorder with complex pathophysiology characterised by recurrent headaches during a period like one month. Only small group of migraine patients (13-31%) experienced transient neurological symptoms that are most frequently visual aura prior to headache onset, yet a majority of patients were migraine without aura (MO) that do not possess the premonitory symptoms. This study explored neurophysiological evidence of the resting-state electroencephalogram (EEG) power, coherence and entropy to support the cortical signals correlate of different migraine phases, and then develop an EEG-based system for predicting migraine attacks. First, we investigated EEG devices, pre-processing and artefact removal methods, and feature extraction technologies, including power, coherence and entropy analysis. Next, we discovered cyclic EEG dynamics of migraine on the cross-sectional basis. The results indicated that EEG power spectral and coherence were significantly increased in the pre-ictal group, relative to EEG data obtained from the inter-ictal group. Inter-ictal patients had decreased EEG power and connectivity relative to healthy controls, which were “normalised” in the pre-ictal patients. Furthermore, on the basis of longitudinal design, we estimated brain dynamics before migraine attacks using a wearable EEG device. The results showed the EEG entropy of individual patients in the pre-ictal phase, resembling normal control subjects, was significantly higher than that in their inter-ictal phase in prefrontal area. That is, the entropy measures identified enhancement or “normalisation” of frontal EEG complexity in pre-ictal phase. Finally, based on these neuroscience discovery of inter- and pre- ictal EEG entropy in individuals, this study proposed a support vector machine (SVM) based system with 76% accuracy to predict migraine attacks. The prediction system characterises the EEG entropy of single (prefrontal) area favoured the application of brain-computer interface in migraine.