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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中平(Chung-Ping Chen),孫啟光(Chi-Kuang Sun) | |
| dc.contributor.author | Yi-Chen Li | en |
| dc.contributor.author | 李易宸 | zh_TW |
| dc.date.accessioned | 2021-06-17T07:00:30Z | - |
| dc.date.available | 2024-08-05 | |
| dc.date.copyright | 2019-08-05 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-01 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72536 | - |
| dc.description.abstract | 重度憂鬱症被認為是一種傾向慢性，病程易惡化的疾病，具有與其他症狀高度合併風險。具有一定比例的重度憂鬱症病患在數個抗憂鬱藥物的治療下沒有好轉，而這類型的病患卻有機會被重複性經顱磁刺激或間歇性脈衝式經顱磁刺激所治療。本研究分析了來自90位重度憂鬱症患者的腦電圖信號，分別觀察重複性經顱磁刺激或間歇性脈衝式經顱磁刺激治療之重度憂鬱症患者的特徵來預測其抗憂鬱反應。腦電信號被分解為5個頻段：delta，theta，alpha，beta和gamma頻段。分頻段之後我們應用數個非線性方法和線性方法做特徵提取，從腦電信號中提取不同面向的特徵。在結果中，首先我們確認了在增加收案數量後，前額葉theta頻段能夠區分重複性經顱磁刺激的治療有效者與無效者；接著我們發現前額葉delta頻段以及全頻段也能夠區分重複性經顱磁刺激的治療有效者與無效者；最後我們探索性的發現間歇性脈衝式經顱磁刺激的抗憂鬱反應可能與前額葉beta頻段有關聯。我們使用機器學習與深度學習用於區分重複性經顱磁刺激的治療有效以及無效者，validation accuracy達到91.1%，其中TPR為83.3%，FPR為5%。 | zh_TW |
| dc.description.abstract | Major depressive disorder (MDD) is increasingly recognized as a chronic, deteriorating illness with high comorbidity. A significant proportion of patients with MDD fail to respond to sequential antidepressants. Such treatment-resistant depression can be treated with noninvasive brain stimulation, such as repetitive transcranial magnetic stimulation (rTMS) and intermittent theta-burst stimulation (iTBS). In this study, we analyzed electroencephalograph (EEG) signals from a total of 90 patients with MDD. Antidepressant responses were predicted by observing the features of patients with MDD receiving rTMS or iTBS treatments. EEG signals were decomposed into 5 bands: delta, theta, alpha, beta, and gamma. Feature extraction, including linear and nonlinear methods, was applied to the EEG signals. First, our study demonstrated that frontal theta could be associated with antidepressant responses to rTMS instead of iTBS and sham treatments. Second, frontal delta and all bands (1–60 Hz) were associated with antidepressant responses to rTMS treatment. Third, antidepressant responses to iTBS treatment might be associated with frontal beta waves. Finally, machine learning and deep learning were used for distinguishing between responders and non-responders, and a 91% validation accuracy rate, an 83.3% true positive rate, and a 5.0% false positive rate were achieved. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T07:00:30Z (GMT). No. of bitstreams: 1 ntu-108-R06945031-1.pdf: 3278686 bytes, checksum: e5e170f2065f08932239bf317f6d0e3a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xiii Chapter 1 Introduction 1 1.1 Major Depressive Disorder 1 1.2 Advanced Treatments for MDD Patients 3 1.3 Electroencephalogram 5 1.4 Thesis Motivation 7 1.5 Thesis Organization 8 Chapter 2 Previous Research 9 2.1 Left DLPFC Dysfunction in MDD 9 2.2 Prediction of rTMS Antidepressant Response by EEG 10 2.3 Computerized rACC-Engaging Cognitive Task 13 2.4 Aims and Hypothesis 14 Chapter 3 Study Procedures and EEG Data Acquisition 15 3.1 Psychiatric Evaluations 16 3.2 Clinical Subjects 17 3.3 EEG Data Acquisition 20 Chapter 4 Methodology 21 4.1 Preprocessing 22 4.1.1 EEG Signal Resampling 22 4.1.2 Band-Pass Filter 22 4.1.3 Independent Component Analysis 23 4.2 Feature Extraction 25 4.2.1 Fractal Dimension 26 4.2.2 Correlation Dimension 27 4.2.3 Approximate Entropy 28 4.2.4 Largest Lyapunov Exponent 29 4.2.5 Detrended Fluctuation Analysis 30 4.2.6 Welch Periodogram 31 4.3 Statistical Analysis 31 4.4 Classification 33 4.4.1 Support Vector Machine 33 4.4.2 Adaptive Boosting Algorithm 34 4.4.3 Deep Neural Network 35 Chapter 5 Experimental Results 37 5.1.1 Comparisons of Different EEG Features between Responders and Non-Responders 37 5.1.2 Correlation Analysis 46 5.1.3 Classification Performance 49 Chapter 6 Discussion 58 Chapter 7 Conclusion 62 Chapter 8 Future Work 63 REFERENCE 64 | |
| dc.language.iso | en | |
| dc.title | 基於機器學習架構的經顱磁刺激抗鬱療效預測於重度憂鬱症之腦波即時分析 | zh_TW |
| dc.title | Real Time EEG Analysis for Prediction of Antidepressant Responses of Transcranial Magnetic Stimulation in Major Depressive Disorder Based on Machine Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 李正達(Cheng-Ta Li) | |
| dc.contributor.oralexamcommittee | 陳志宏(Jyh-Horng Chen) | |
| dc.subject.keyword | 重度憂鬱症,腦電圖,重複性經顱磁刺激,間歇性脈衝式經顱磁刺激,機器學習,深度學習, | zh_TW |
| dc.subject.keyword | Major depressive disorder,Electroencephalograph,Repetitive transcranial magnetic stimulation,Intermittent theta-burst stimulation,Machine learning,Deep learning, | en |
| dc.relation.page | 74 | |
| dc.identifier.doi | 10.6342/NTU201902399 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-02 | |
| 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 |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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