偏頭痛是常見且會嚴重影響生活品質的疾病，患者中約有30%-50%有睡眠障礙，若未及時治療偏頭痛及其共病，睡眠品質不佳容易惡化為失眠。過往使用腦電圖(Electroencephalography, EEG)訊號進行偏頭痛(Migraine)的研究中，主要為偏頭痛與健康受測者的比較、偏頭痛階段或有無預兆的比較，尚無使用EEG針對偏頭痛有無共病睡眠品質不佳的研究，因次本論文共收集19位有睡眠品質不佳的偏頭痛患者(MwPSQ)、17位無睡眠品質不佳的偏頭痛患者(MwoPSQ)和15位健康對照組(HC)在心算(mental arithmetic, MA)、心算前閉眼放鬆(eye-closed resting state before the MA task, Pre-resting)的EEG進行分析，並使用Pre-resting作為基準，得到校正(Basline correction)後的MA(校正)等三種狀態，希望藉由機器學習演算法找出可用於區分有無共病睡眠品質不佳之偏頭痛患者的獨特腦波生物標記。
由於偏頭痛患者與失眠患者皆有大腦功能性連結異常的狀況，故本論文使用連結性特徵作為EEG評估手段，分別採用相干性(Magnitude-squared coherence, Coh)、虛部相干性(Imaginary part of coherence, ImC)及相位延遲指數(Phase lag index, PLI)進行特徵抽取，並使用費雪準則進行特徵篩選，線性鑑別分析(Linear Discriminant Analysis, LDA)及非線性支持向量機(Nonliner Support Vector Machine,Nonlinear SVM)進行分類，最後整合分類結果最佳的特徵組並輸出腦波評估指標(EEG Assessment Index,EAI)，再與匹茲堡睡眠品質量表（The Pittsburgh Sleep Quality Index, PSQI）進行相關性分析。研究結果顯示：一、使用PLI分類結果最佳；二、於MA及MA(校正)下分類表現優於Pre-resting；三、Nonlinear SVM分類表現優於LDA；四、使用PLI特徵可精準判斷偏頭痛患者有無睡眠品質不佳， MA下使用LDA：MwPSQ vs MwoPSQ可達97.4%分類率，於Pre-resting下使用SVM亦可達97.4%分類率；五、EAI可與PSQI總分及部分細項達到高度相關性，可幫助醫師快速評估患者病症嚴重程度及追蹤治療成效。

Migraine is a common disease that affect patients’s quality of life, and about 30%-50% of migraines are affected by sleep disorders. Both poor sleep quality and insomnia are sleep disorders. If not treated properly, poor sleep quality is likely to worsen and become insomnia.There have been many studies using EEG signals to conduct migrain research,but the main topics are about difference between migraineurs and healthy controls,difference between migraineurs with aura and without aura,or between each phase during migraine attack.We haven’t discover EEG research wich focus on the difference between migraineurs that with and without poor sleep quality.Therefore, in this study we collects the EEG of 19 migraineurs with poor sleep quality(MwPSQ), and 17 migraineurs without poor sleep quality(MwoPSQ), and 15 healthy controls(HC) during mental arithmetic(MA), eye-closed resting state before the MA task( Pre-resting), and using Pre-resting as baseline to obtain MA(baseline correction).This study aims to find the unique EEG biomarker of MwPSQ and MwoPSQ by machine learning algorithm.
This study use Magnitude-squared coherence(Coh), Imaginary part of coherence(ImC) and Phase lag index(PLI) as EEG feature.Feature selection was done by Fisher criterion, and classification was done by Linear Discriminant Analysis(LDA) and Nonlinear support vector machin(Nonlinear SVM).The features with best classification results will be intergrated to obtain the EEG Assessment Index(EAI).We use Pearson correlation coefficient to evaluate the correlation beween EAI and The Pittsburgh Sleep Quality Index(PSQI).Results show that 1.PLI outperforms ImC and Coh, 2. MA or MA(baseline correction) is better than just using resting-state, 3.Nonliner SVM outperforms LDA, 4.By using PLI in MA : MwPSQ vs MwoPSQ, LDA can achieve a Balance classification rate of 97.4% (B-CR).And in Pre-resting, SVM can achieve B-CR of 97.4%, 5.EAI is highly correlated with PSQI,it can be a new approach for doctors to evaluate patients, and it can also be used to track the therapeutic effect.

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