機器學習和腦電功能性網路應用於重度憂鬱症與頑固性癲癇的治療效果之預測

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姓名

林子平(Tzu-Ping Lin) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

機器學習和腦電功能性網路應用於重度憂鬱症與頑固性癲癇的治療效果之預測
(Prediction of Treatment Effects for Major Depressive Disorder and Refractory Epilepsy Using Machine Learning and EEG Functional Networks)

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至系統瀏覽論文 (2026-7-25以後開放)

摘要(中)

臨床變數(Clinical Variables)是疾病診斷和治療效果預測的重要依據，然而臨床變數有其侷限性，像是重鬱症(Major depressive disorder, MDD)和癲癇(Epilepsy)這類常見精神或神經疾病，臨床變數無法有效反應大腦的情況。
本研究從腦電圖(Electroencephalography)中提取功能網路(Functional Connectivity)和功率(Power)，並結合臨床變數，使用留一交叉驗證(Leave-One-Out Cross-Validation, LOOCV)和機器學習的分類器進行訓練，應用於三個子題目，分別為「重鬱症之診斷」、「重鬱症之療效預測」和「頑固性癲癇之療效預測」。
「重鬱症之診斷」的目標為「透過腦電圖和臨床變數輔助診斷重鬱症」，共蒐集到77位病患和46健康者的腦電圖以及臨床變數。最好的診斷效果為絕對功率(Absolute Power)配上Medium KNN時，精確率(Precision)為0.881、召回率(Recall)為0.961、特異性(Specificity)為0.783、準確率(Accuracy)為0.894、F1 Score為0.919、曲線下面積(Area Under Curve, AUC)為0.876。
「重鬱症之療效預測」的目標為「透過腦電圖和臨床變數輔助預測重鬱症患者接受治療後4、6和8週的療效」，共蒐集到77位病患治療前的腦電圖、治療後一週的腦電圖以及臨床變數。
預測治療後4週的療效時，最好的診斷效果為治療後一週腦電圖(W1)的相位鎖定值(Phase Locking Value，PLV)和Absolute Power配上Fine Tree或Medium Tree時，Precision為0.704、Recall為0.792、Specificity為0.849、Accuracy為0.831、F1 Score為0.745、AUC為0.818。
預測治療後6週的療效時，最好的診斷效果為治療後一週腦電圖特徵減去治療前腦電圖特徵(W1-W0)的PLV、相對功率(Relative Power)和臨床變數配上Coarse Gaussian SVM時，Precision為0.724、Recall為0.724、Specificity為0.742、Accuracy為0.733、F1 Score為0.724、AUC為0.772。
預測治療後8週的療效時，最好的診斷效果為治療後一週腦電圖特徵減去治療前腦電圖特徵(W1-W0)的Absolute Power和臨床變數配上Coarse Tree時， Precision為0.675、Recall為0.849、Specificity為0.519、Accuracy為0.700、F1 Score為0.757、AUC為0.790。
「頑固性癲癇之療效預測」的目標為「過腦電圖輔助預測頑固性癲癇患者接受生酮飲食治療後3和6個月的療效」，共蒐集到90位病患的腦電圖。
預測治療後3個月的療效時，最好的診斷效果為PLV和Relative Power配上Fine Tree或Medium Tree時，Precision為0.831、Recall為0.817、Specificity為0.667、Accuracy為0.767、F1 Score為0.824、AUC為0.760。
預測治療後6個月的療效時，最好的診斷效果為PLV和Absolute Power配上Linear SVM時，Precision為0.800、Recall為0.963、Specificity為0.000、Accuracy為0.776、F1 Score為0.874、AUC為0.752。

摘要(英)

Clinical variables are crucial for diagnosing diseases and predicting treatment outcomes. However, they have limitations, particularly for common psychiatric or neurological disorders like Major Depressive Disorder (MDD) and Epilepsy, where clinical variables cannot effectively reflect the brain′s condition.
This study extracts functional connectivity and power from electroencephalography (EEG) and combines these with clinical variables. The data are then trained using Leave-One-Out Cross-Validation (LOOCV) and machine learning classifiers, applied to three sub-topics: "Diagnosis of Major Depressive Disorder," "Prediction of Treatment Efficacy for Major Depressive Disorder," and "Prediction of Treatment Efficacy for Refractory Epilepsy.
The goal of the "Diagnosis of Major Depressive Disorder" is to "assist in diagnosing MDD through EEG and clinical variables." A total of 77 patients and 46 healthy individuals′ EEG and clinical variables were collected. The best diagnostic result was achieved using Absolute Power with Medium KNN, yielding a Precision of 0.881, Recall of 0.961, Specificity of 0.783, Accuracy of 0.894, F1 Score of 0.919, and AUC of 0.876.
The goal of the "Prediction of Treatment Efficacy for Major Depressive Disorder" is to "assist in predicting the efficacy of treatment after 4, 6, and 8 weeks using EEG and clinical variables." EEGs before treatment, EEGs one week after treatment, and clinical variables from 77 patients were collected.
For predicting treatment efficacy at 4 weeks, the best result was achieved using W1 EEG PLV and Absolute Power with Fine Tree or Medium Tree, yielding a Precision of 0.704, Recall of 0.792, Specificity of 0.849, Accuracy of 0.831, F1 Score of 0.745, and AUC of 0.818.
For 6 weeks, the best result was achieved using W1-W0 EEG PLV, Relative Power, and clinical variables with Coarse Gaussian SVM, yielding a Precision of 0.724, Recall of 0.724, Specificity of 0.742, Accuracy of 0.733, F1 Score of 0.724, and AUC of 0.772.
For 8 weeks, the best result was achieved using W1-W0 EEG Absolute Power and clinical variables with Coarse Tree, yielding a Precision of 0.675, Recall of 0.849, Specificity of 0.519, Accuracy of 0.700, F1 Score of 0.757, and AUC of 0.790.
The goal of the "Prediction of Treatment Efficacy for Refractory Epilepsy" is to "assist in predicting the efficacy of ketogenic diet treatment after 3 and 6 months using EEG." EEGs from 90 patients were collected.
For predicting treatment efficacy at 3 months, the best result was achieved using PLV and Relative Power with Fine Tree or Medium Tree, yielding a Precision of 0.831, Recall of 0.817, Specificity of 0.667, Accuracy of 0.767, F1 Score of 0.824, and AUC of 0.760.
For 6 months, the best result was achieved using PLV and Absolute Power with Linear SVM, yielding a Precision of 0.800, Recall of 0.963, Specificity of 0.000, Accuracy of 0.776, F1 Score of 0.874, and AUC of 0.752.

關鍵字(中)

★ 腦電圖
★ 重鬱症
★ 頑固性癲癇
★ 機器學習

關鍵字(英)

★ Electroencephalography
★ Major Depressive Disorder
★ Refractory Epilepsy
★ Machine Learning

論文目次

摘要 i
Abstract iv
致謝 vii
目錄 viii
圖目錄 xiii
表目錄 xv
第一章緒論 1
1.1研究動機 1
1.2重鬱症 2
1.3頑固性癲癇 3
1.4腦電圖 4
1.5功能網路 5
1.6功率頻譜密度 6
1.7文獻回顧 6
1.7.1重鬱症 6
1.7.2頑固性癲癇 7
1.8論文架構 8
第二章研究方法 9
2.1重鬱症之診斷 9
2.1.1資料集 9
2.1.2腦電圖前處理 10
2.1.3特徵提取 11
2.1.4機器學習 15
2.2重鬱症之療效預測 20
2.2.1資料集 20
2.2.2腦電圖前處理 22
2.2.3特徵提取 23
2.2.4機器學習 26
2.3頑固性癲癇之療效預測 30
2.3.1資料集 30
2.3.2腦電圖前處理 31
2.3.3特徵提取 33
2.3.4機器學習 35
2.4評估指標 38
第三章研究結果 42
3.1重鬱症之診斷 42
3.2重鬱症之療效預測 44
3.2.1 W4的預測結果 44
3.2.2 W6的預測結果 45
3.2.3 W8的預測結果 46
3.3頑固性癲癇之療效預測 47
3.3.1 M3的預測結果 47
3.3.2 M6的預測結果 49
第四章討論 51
4.1腦電圖和臨床變數結合之效果 51
4.1.1重鬱症之診斷 51
4.1.2重鬱症之療效預測 53
4.2資料增量 54
4.2.1 SMOTE 55
4.2.2 Borderline-SMOTE 57
4.2.3 ADASYN 59
4.2.4 Safe-Level-SMOTE 61
4.3特徵選擇 65
4.4不同的功能網路計算方式 68
4.5遇到的問題 70
4.6未來發展的方向 71
4.6.1對特徵使用Z分數標準化 71
4.6.2使用深度卷積網路進行特徵提取 71
第五章結論 72
參考文獻 74
附錄 A. 分類器評估指標的詳細數據 79
A.1重鬱症之診斷 79
A.2重鬱症之療效預測 95
A.2.1治療後4週 95
A.2.2治療後6週 143
A.2.3治療後8週 191
A.3頑固性癲癇之療效預測 239
A.3.1治療後3個月 239
A.3.2治療後6個月 247
附錄B. 受試者的詳細資料 255
B.1重鬱症之診斷 255
B.2重鬱症之療效預測 257
B.3頑固性癲癇之療效預測 260

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指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2024-7-30

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