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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中平(Chung-Ping Chen) | |
| dc.contributor.author | Chi-Sheng Chen | en |
| dc.contributor.author | 陳麒升 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:40Z | - |
| dc.date.copyright | 2021-11-10 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-01 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82206 | - |
| dc.description.abstract | 重度憂鬱症現今被認為是一種慢性惡化的精神疾病，並且具有與其他症狀甚至是自殺意念併發的風險。有一定比例的重度憂鬱症患者在嘗試過數種抗憂鬱藥物的治療過後並無顯著好轉，而此類型的病患被發現其有一定機率能被經顱磁刺激所治療。目前經顱磁刺激較為常見的類型有重複性經顱磁刺激與間歇性θ脈衝式經顱磁刺激兩種，而是否能為患者於臨床治療前預測各參數對各自病人的抗憂鬱反應並提供個人化精準有效的診療參數建議在未來將會是一個重要的技術。本研究利用129位重度憂鬱症患者的臨床腦電波資料訓練數種傳統與量子機器學習演算法並建構全新的深度學習模型來對兩種模式的經顱磁刺激療效進行訓練與預測，尤其針對幾乎沒有前人研究的間歇性θ脈衝式經顱磁刺激進行療效預測，並在包含模型類型的選取、深度學習模型的損失函數等等地方盡最大努力下避免模型過擬合。在結果中，本研究所提出的新式注意力機制卷積時間序列深度學習模型在常見的深度學習模型中預測兩種經顱磁刺激療效的效果均為最好。傳統機器學習方法中則使用了集成模型中的套袋與助推兩類模型，本研究也提出了一個能夠優化模型敏感度的資料前處理演算法，其綜合效果也較前人的支持向量機效果好。最後本研究也在真實的超導量子電腦上訓練了量子機器學習模型，並由結果證明其效果比同種傳統演算法好上許多。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:40Z (GMT). No. of bitstreams: 1 U0001-2906202117440700.pdf: 5917102 bytes, checksum: 0c5cff8a9b57793ad084924ff6ca8ed8 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員審定書（掃描本） iii 誌謝 iv 中文摘要 1 ABSTRACT 2 CONTENTS 4 LIST OF FIGURES 8 LIST OF TABLES 12 Chapter 1 Introduction 16 1.1 Thesis Motivation 16 1.2 Major Depressive Disorder (MDD) 17 1.3 Advanced Treatments for MDD Patients 18 1.4 Electroencephalogram 21 1.4.1 Prediction of TMS Response by EEG 21 1.4.2 Prediction of TMS Response by Artificial Intelligence with EEG Data 22 1.4.3 The Overfitting Problem on SVM 24 1.5 Thesis Organization 27 Chapter 2 Previous Research 28 2.1 Rostral Anterior Cingulate Cortex (rACC)-Engaging Cognitive Task (RECT) 28 2.2 Machine Learning on MDD EEG Data 30 2.3 Deep Learning on MDD EEG Data 31 2.4 Quantum Machine Learning on Time Series Data 31 2.5 Previous Work is Overfitting on the New Dataset 33 2.6 Bagging/Boosting is Less Overfitting than SVM 34 2.7 Thesis Aims and Hypothesis 35 Chapter 3 Methodology 37 3.1 EEG Data Acquisition 38 3.1.1 Psychiatric Evaluations 38 3.1.2 EEG Data Acquisition 39 3.1.3 MDD EEG Datasets 40 3.1.3.1 Randomized Controlled Trial Data (RCTD) (Previous Dataset) 42 3.1.3.2 Clinical OPD dataset 43 3.2 EEG Data Preprocessing 45 3.2.1 EEG Signal Resampling 45 3.2.2 Band Pass Filter 46 3.2.3 Independent Component Analysis 46 3.3 ACTSNet: Attentional Convolution Time Series Neural Network 48 3.3.1 ACTSNet Architecture 50 3.3.2 Prototypical Learning 52 3.4 Multivariate Time Series Classification (MTSC) Methods 55 3.4.1 Bag-of-SFA-Symbols Ensemble (BOSS Ensemble) 55 3.5 Classical Machine Learning Methods 56 3.5.1 Data Preprocessing 57 3.5.2 Feature Extraction 59 3.5.2.1 Largest Lyapunov Exponent 60 3.5.2.2 Detrended Fluctuation Analysis 61 3.5.2.3 Katz Fractal Dimension (KFD) and Higuchi Fractal Dimension (HFD) 62 3.5.2.3.1 Katz Fractal Dimension 62 3.5.2.3.2 Higuchi Fractal Dimension 63 3.5.2.4 Approximate Entropy 64 3.5.2.5 Welch Periodogram 65 3.5.3 Feature Selection 65 3.5.4 Logistic Regression 66 3.5.5 Support Vector Machine (SVM) 67 3.5.6 Bagging and Boosting Classification Models 69 3.5.6.1 XGBoost 70 3.5.6.2 CatBoost 73 3.5.6.3 Random Forest 76 3.5.7 The Booster Transformation 80 3.6 Quantum Machine Learning Methods 82 3.6.1 Data Preprocessing, Feature Extraction and Selection 83 3.6.2 Introduction to Quantum Computing 84 3.6.3 Quantum Support Vector Machine (QSVM) 86 Chapter 4 Experiment Results 93 4.1 Deep Learning Results 93 4.1.1 Deep Machine Learning Results 93 4.2 Multivariate Time Series Classification (MTSC) Results 98 4.2.1 MTSC Results 98 4.2.2 BOSS Ensemble vs. ACTSNet 99 4.3 The Statistically Significant features for Machine Learning on OPD Dataset 100 4.3.1 OPD Dataset 100 4.3.2 Mixed Dataset 105 4.3.3 Dataset Comparison 113 4.4 Classical Machine Learning Results 115 4.4.1 Classical Machine Learning Results 115 4.4.1.1 Compare with Linear Method and SVM on the Clinical Trial Dataset 115 4.4.1.2 SVM and the Overfitting Problem 118 4.4.1.3 Compare with Previous Work on the Clinical Trial Dataset 119 4.4.1.4 Compare with Previous Work on the OPD Dataset 121 4.4.1.5 Compare with Previous Work on the Mixed Dataset 122 4.4.2 Booster Transformation Results 123 4.4.2.1 Compare Booster with Non-Booster Methods on Random Forest with rTMS Mixed Dataset 124 4.4.2.2 Compare Booster with Non-Booster methods on XGBoost with rTMS Mixed Dataset 125 4.4.2.3 Compare Booster with Non-Booster Methods on CatBoost with rTMS Mixed Dataset 127 4.4.2.4 Compare Booster with Non-Booster Methods on Random Forest with iTBS Mixed Dataset 128 4.4.2.5 Compare Booster with Non-Booster Methods on XGBoost with iTBS Mixed Dataset 130 4.4.2.6 Compare Booster with Non-Booster Methods on CatBoost with iTBS Mixed Dataset 131 4.4.3 Booster-Classical Machine Learning Results 134 4.4.3.1 Compare with Previous Work on the OPD Dataset 134 4.4.3.2 Compare with Previous Work on the Mixed Dataset 135 4.4.4 Feature Importance and Digital Biomarkers 137 4.4.4.1 rTMS Data on Bagging and Boosting Machine Learning Algorithms 137 4.4.4.2 iTBS OPD Data on Bagging and Boosting Machine Learning Algorithms 140 4.5 Quantum Machine Learning Results 143 Chapter 5 Conclusion 145 Chapter 6 Future Work 147 Reference 149 " | |
| dc.language.iso | en | |
| dc.subject | 重度憂鬱症 | zh_TW |
| dc.subject | 量子計算 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 經顱磁刺激 | zh_TW |
| dc.subject | 腦電圖 | zh_TW |
| dc.subject | Deep learning | en |
| dc.subject | Quantum computing | en |
| dc.subject | Transcranial magnetic stimulation | en |
| dc.subject | Electroencephalography | en |
| dc.subject | Major depressive disorder | en |
| dc.subject | Machine learning | en |
| dc.title | 基於注意力機制之時間序列原型卷積神經網路與傳統及量子機器學習模型應用於重度憂鬱症腦波之經顱磁刺激抗憂鬱療效預測與分析 | zh_TW |
| dc.title | EEG Analysis for Prediction of Antidepressant Responses of Transcranial Magnetic Stimulation in Major Depressive Disorder Based on Attentional Convolution Time Series Prototypical Neural Network Model and Classical/Quantum Machine Learning Approaches | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0003-0807-0217 | |
| dc.contributor.coadvisor | 李正達(Cheng-Ta Li) | |
| dc.contributor.oralexamcommittee | 黃聖傑(Hsin-Tsai Liu),楊智傑(Chih-Yang Tseng) | |
| dc.subject.keyword | 重度憂鬱症,腦電圖,經顱磁刺激,機器學習,深度學習,量子計算, | zh_TW |
| dc.subject.keyword | Major depressive disorder,Electroencephalography,Transcranial magnetic stimulation,Machine learning,Deep learning,Quantum computing, | en |
| dc.relation.page | 167 | |
| dc.identifier.doi | 10.6342/NTU202101201 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-07-01 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-06-29 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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