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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中平 | |
| dc.contributor.author | Sophia Ina Wu | en |
| dc.contributor.author | 吳伊娜 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:37:58Z | - |
| dc.date.available | 2017-08-02 | |
| dc.date.copyright | 2017-08-02 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-31 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67566 | - |
| dc.description.abstract | 重度憂鬱症(Major depressive disorder)是一個慢性、退化性且患者容易出現高度失能風險的疾病。為了讓臨床醫師能夠在治療前藉由客觀的方式決定該給予患者何種合適的治療方法，在本篇論文內提出並建立一個能夠在治療前預測患者治療效果之即時自動化分析系統。在此系統中，我們將應用時頻分析中的小波轉換及非線性分析的方法：最大李亞普諾夫指數 (Largest Lyapunov Exponent)、消除趨勢波動分析法 (Detrended Fluctuation Analysis)、分形維數 (Fractal Dimension)、關聯維數 (Correlation Dimension)及近似熵 (Approximate Entropy)來擷取腦波圖(Electroencephalography)訊號的特徵值，藉以區分治療之療效。為了驗證這些非線性分析之方法結合小波轉換能否作為區分憂鬱症治療之療效，本研究利用上述方法來擷取出特徵向量，再運用無母數分析、相關性分析及混淆矩陣來評估分類表現及設定區分是否有療效的最佳閥值。另外，我們將此系統分析自動化以及可以即時判別療效(40秒之內，加快45倍)，並預期能協助醫生快速的做治療前之療效預測。 | zh_TW |
| dc.description.abstract | Major depressive disorder (MDD) is increasingly to be recognized as a chronic, deteriorating illness with the high risk to obtain comorbidity. In order to provide clinicians with a subjective approach to decide appropriate treatments for MDD patients, a real time automatic detection system for predicting the antidepressant responses is of important. Wavelet Transform and nonlinear methods - Largest Lyapunov Exponent (LLE), Detrended Fluctuation Analysis (DFA), Fractal Dimension (FD), Correlation Dimension (CD) and Approximate Entropy (ApEn) were applied to extract the features from electroencephalography (EEG) activities in antidepressant responses. Non-parametric analysis, correlation analysis and confusion matrix were employed to evaluate the performance of classifying and decide the optimal threshold for discrimination. Moreover, the system is built to aid clinicians’ in prediction of the antidepressant responses before treatments by an automatic real time detection system and the results can be viewed within 40 seconds (45X). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:37:58Z (GMT). No. of bitstreams: 1 ntu-106-R04945012-1.pdf: 2530052 bytes, checksum: 251e74fb53cc1ab3c693605991013232 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES viii LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Major Depressive Disorder 1 1.2 Thesis Motivation 5 1.3 Thesis Organization 8 Chapter 2 Background 11 2.1 Electroencephalogram 11 2.2 Antidepressant Effects and Pre-Treatment Neural Activity 13 2.3 Treatments for MDD 16 2.4 Computerized rACC-Engaging Cognitive Task 18 2.5 EEG Data Acquisition 19 2.6 Psychiatric Evaluations 20 2.7 Clinical Subjects and Clinical Design 21 2.8 Statistics for EEG Signals 22 2.9 Neuroscan 4.3 Software 23 2.10 EEGLAB Open Source 25 Chapter 3 Methodology 27 3.1 Frequency Analysis - Fast Fourier Transform 28 3.2 Preprocessing 29 3.2.1 Independent Component Analysis 29 3.2.2 Filtering 30 3.3 Time-Frequency Analysis – Wavelet Transform 31 3.4 Features Extraction 33 3.4.1 Largest Lyapunov Exponent 34 3.4.2 Detrended Fluctuation Analysis 36 3.4.3 Fractal Dimension 38 3.4.4 Correlation Dimension 41 3.4.5 Approximate Entropy 43 3.5 Spec and Working Environment 44 Chapter 4 Experimental Results 45 4.1 Non-Parametric Analysis 46 4.2 Correlation Analysis with Response Rate 53 4.3 Classification Performances 56 Chapter 5 Discussion 61 5.1 Nonlinear Features 62 5.2 Relation with DLPFC 63 5.3 Nonlinear Methods in Different Diseases 64 Chapter 6 Conclusions 65 Chapter 7 Future Work 67 REFERENCE 69 | |
| dc.language.iso | en | |
| dc.subject | 腦波 | zh_TW |
| dc.subject | 分形維數 | zh_TW |
| dc.subject | 消除趨勢波動分析法 | zh_TW |
| dc.subject | 小波轉換最大李亞普諾夫指數 | zh_TW |
| dc.subject | 重度憂鬱症 | zh_TW |
| dc.subject | Largest Lyapunov Exponent | en |
| dc.subject | Electroencephalography | en |
| dc.subject | Major depressive disorder | en |
| dc.subject | Fractal Dimension | en |
| dc.subject | Wavelet Transform | en |
| dc.subject | Detrended Fluctuation Analysis | en |
| dc.title | 臨床憂鬱症之腦波即時分析及輔助預測療效之系統 | zh_TW |
| dc.title | Real Time Computer Aided Detection System for the Prediction of Clinical Antidepressant Responses | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 李正達 | |
| dc.contributor.oralexamcommittee | 莊曜宇,沈家平 | |
| dc.subject.keyword | 重度憂鬱症,腦波,小波轉換最大李亞普諾夫指數,消除趨勢波動分析法,分形維數, | zh_TW |
| dc.subject.keyword | Major depressive disorder,Electroencephalography,Wavelet Transform,Largest Lyapunov Exponent,Detrended Fluctuation Analysis,Fractal Dimension, | en |
| dc.relation.page | 77 | |
| dc.identifier.doi | 10.6342/NTU201702285 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-31 | |
| 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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| ntu-106-1.pdf 未授權公開取用 | 2.47 MB | Adobe PDF |
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