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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中平(Chung-Ping Chen) | |
| dc.contributor.author | Chia-Hsuan Lin | en |
| dc.contributor.author | 林家軒 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:06:03Z | - |
| dc.date.copyright | 2022-07-06 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-04 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84191 | - |
| dc.description.abstract | 重度憂鬱症是影響人類的一種常見的世紀重大疾病。而具有一定比率的重度憂鬱症患者在數個抗憂鬱症藥物的治療下沒有好轉就會被定義成頑固型憂鬱症。本研究分析199位受試者的靜息態功能性磁共振造影訊號，包含頑固型憂鬱症患者（無效藥物3種以上）、非頑固型憂鬱症患者（無效藥物2種以下）和健康受試者，我們想要建構一套輔助醫師預測是否為憂鬱症患者並依照嚴重程度預測是否為頑固型憂鬱症患者或是非頑固型憂鬱症患者的系統。 我們從靜息態功能性磁共振造影訊號中提取三種不同面向的特徵，包含區域同質性特徵、圖論式網路特徵和皮爾森相關性特徵，通過結合大量的特徵再選出最為相關且重要的特徵，經由我們的機器學習演算法能夠成功預測並分別在三種模型上（預測是否憂鬱症、預測是否頑固型憂鬱症、複雜情況預測是否頑固型憂鬱症）得到的準確率是84.3%、88.4% 和78.6%。並依照重要特徵能夠找出憂鬱症特徵分布在額葉、顳葉和頂葉，而頑固性嚴重度特徵主要分布在額葉和顳葉。 最後，我們透過深度學習來簡化處理資料步驟，並分別在三種模型上的準確度為87.5%、84.2% 和71.0%。 關鍵字：重度憂鬱症、頑固型憂鬱症、靜息態功能性磁共振造影、機器學習、深度學習 | zh_TW |
| dc.description.abstract | Major depressive disorder (MDD) is a common and a major disease of the century that affects human beings. Treatment resistant depression (TRD) is described as a fraction of people with MDD who do not cure after being treated with various antidepressant medicines. This study analyzed the resting-state fMRI (rs-fMRI) signals of 199 subjects, including patients with TRD (more than 3 ineffective drugs), Non-TRD (with less than 2 ineffective drugs) and healthy subjects. We want to construct a system that assists physicians in predicting depression and predicting TRD or Non-TRD according to severity. We extracted three different oriented features from the rs-fMRI signals, including correlation-based feature, Graph-based feature and Between-region connectivity-based features, and then selected the most relevant and important features by combining a large number of features. Our machine learning algorithm can predict the class of subject and the accuracy obtained on the three models (prediction of depression, prediction of resistant severity, and complex case prediction of resistant severity) are 84.3% and 88.4%. and 78.6%. And according to the important features, it can be found that the features of depression are distributed in the frontal lobe, temporal lobe and parietal lobe, and the features of resistant severity are mainly distributed in the frontal and temporal lobe. Finally, we use deep learning to simplify the data processing step and achieve 87.5%, 84.2% and 71.0% accuracy on the three models, respectively. Keywords: major depression, refractory depression, resting-state functional magnetic resonance imaging, machine learning, deep learning | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:06:03Z (GMT). No. of bitstreams: 1 U0001-0307202214463100.pdf: 3184783 bytes, checksum: ccd33e034b5bfb74fe29b93b640f2c4d (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試審定書 i 誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES xi LIST OF TABLES xv Chapter 1 Introduction 1 1.1 Major Depressive Disorder 1 1.2 Treatment Resistant Depression 1 1.3 Advanced Treatment for depression patients 3 1.4 Resting-state Functional MRI 6 1.5 Thesis Motivation 7 1.6 Thesis organization 8 Chapter 2 Previous research 9 2.1 DLPFC dysfunction in MDD 9 2.2 Machine Learning on MDD/TRD rs-fMRI data 10 2.3 Deep Learning on MDD/TRD rs-fMRI data 11 2.4 Aims and hypothesis 12 Chapter 3 Data acquisition 14 3.1 Psychiatric Evaluations 14 3.2 Clinical Subject 15 3.3 Rs-fMRI Data Acquisition 17 3.4 Classification Model 19 Chapter 4 Methodology 20 4.1 Machine Learning method overview 20 4.2 Preprocessing 21 4.2.1 Slice timing 22 4.2.2 Realignment 23 4.2.3 Nuisance regression 24 4.2.4 Detrend 25 4.2.5 Filter 25 4.2.6 Normalization 26 4.2.7 Smooth 27 4.3 Feature extraction 28 4.3.1 Correlation-based features 29 4.3.1.1 Regional Homogeneity (ReHo) 29 4.3.1.2 fractional Amplitude Low Frequency Fluctuation (fALFF) 30 4.3.2 Graph-based features 32 4.3.2.1 Betweenness centrality 34 4.3.2.2 Characteristic path [74] 34 4.3.2.3 Clustering coefficient [74] 34 4.3.2.4 Degree 35 4.3.2.5 Local efficiency 35 4.3.2.6 Eigenvector centrality [77] 35 4.3.2.7 K-coreness centrality [77, 78] 36 4.3.2.8 Community structure Newman (Modularity)[79] 36 4.3.2.9 Within module degree z-score 37 4.3.2.10 Eccentricity [80] 37 4.3.2.11 Participation coefficient [81] 37 4.3.2.12 Subgraph centrality [82, 83] 38 4.3.3 Between-region connectivity-based features 38 4.3.4 Summary of Features 39 4.4 Data splitting 40 4.5 Standardization 40 4.6 Feature Selection 41 4.7 K-Cross Validation 43 4.8 Model training 44 4.8.1 Support Vector Machine (SVM) 44 4.8.2 Random Forest (RF) 45 4.8.3 XGboost 46 4.8.4 Logistic Regression (LR) 46 4.8.5 Ensemble model 47 4.9 Deep Learning method Overview 48 4.10 TapNet structure 49 Chapter 5 Experimental Results 54 5.1 Evaluation Method 54 5.2 Feature Selection Result 57 5.3 Performance of Classifiers 59 5.4 Final Result 60 5.5 Features of Importance 63 5.6 Result of Prevents Overfitting of TapNet 67 5.7 Classification Results of TapNet 68 5.8 Comparison with Traditional Method 70 5.8.1 Feature selected by our team 71 5.8.2 P-value corrected for multiple comparison 72 5.9 Comparison with Single type feature 73 5.10 Comparison with different Deep Learning model 74 5.11 Comparison with Machine Learning and Deep Learning 76 Chapter 6 Conclusion 78 Chapter 7 Future work 80 Reference 81 | |
| dc.language.iso | en | |
| dc.subject | 靜息態功能性磁共振造影 | zh_TW |
| dc.subject | 靜息態功能性磁共振造影 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 頑固型憂鬱症 | zh_TW |
| dc.subject | 重度憂鬱症 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | refractory depression | en |
| dc.subject | esting-state functional magnetic resonance imaging | en |
| dc.subject | major depression | en |
| dc.subject | machine learning | en |
| dc.subject | deep learning | en |
| dc.title | 基於機器學習和深度學習應用於預測重度憂鬱症之靜息態功能性磁共振造影訊號 | zh_TW |
| dc.title | Using rs-fMRI Signals for Prediction of Major Depressive Disorder Based on Machine Learning and Deep Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 李正達(Cheng-Ta Li) | |
| dc.contributor.oralexamcommittee | 鍾孝文(Hsiao-Wen Chung),陳文翔(Wen-Shiang Chen) | |
| dc.subject.keyword | 重度憂鬱症,頑固型憂鬱症,靜息態功能性磁共振造影,靜息態功能性磁共振造影,機器學習,深度學習, | zh_TW |
| dc.subject.keyword | major depression,refractory depression,esting-state functional magnetic resonance imaging,machine learning,deep learning, | en |
| dc.relation.page | 96 | |
| dc.identifier.doi | 10.6342/NTU202201254 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-07-04 | |
| 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 | 2022-07-06 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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