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系統識別號 U0026-0202201810214400
論文名稱(中文) 基於視覺刺激下誘發思覺失調症患者情緒的腦波熵值與腦部MRI之分析研究
論文名稱(英文) EEG entropy analysis on visual stimulation-evoked emotion and MR images in schizophrenia patients
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 106
學期 1
出版年 107
研究生(中文) 朱玟霖
研究生(英文) Wen-Lin Chu
學號 P88001083
學位類別 博士
語文別 英文
論文頁數 66頁
口試委員 指導教授-鄭國順
口試委員-孫永年
口試委員-詹寶珠
口試委員-林慶波
口試委員-李宗南
口試委員-黃敏偉
口試委員-王作仁
中文關鍵字 思覺失調症  活性與負性症狀量表  國際情感圖片系統  支持向量機 
英文關鍵字 Schizophrenia  Positive and Negative Syndrome Scale  International Affective Picture System  Support Vector Machine 
學科別分類
中文摘要 思覺失調症為精神醫學中典型的精神疾病,病患需除了承受幻覺與妄想等症狀之苦,同時社會也承擔相當高的經濟成本;目前有許多的文獻針對思覺失調症的治療、診斷、預後、傷害等相關研究進行探討。而研究探討的工具中則有腦電圖以及核磁共振影像最具特色。現今,腦波是功能性的神經學檢查,對於非腦結構性病變引起的腦功能異常具有診斷的效果。另外核磁共振影像能以非侵入性地方式取得大腦內部組織的情形,因此也成為許多專家學者研究分析的工具。本研究針對思覺失調症患者與正常人,使用腦電圖與核磁共振影像作為對照分析。針對腦波的部分,使用國際情緒圖片系統的圖片作為受測者之情緒誘發,並同步收集腦波。使用不同的腦波點與頻率以及熵值做為特徵值,以辨識正常與中度以及重度的思覺失調症患者。針對核磁共振影像的部分,使用 voxel-based morphometry (VBM) 將腦影像分解為灰質和白質,並且映射至 Montreal Neurological Institute (MNI) 的空間,之後再使用 DARTEL 校準影像。最後採用joint source-based morphometry (jICA) 對灰質和白質兩組影像進行分析。最後將腦部結構影像與威斯康辛卡片分類測驗的錯誤率做為特徵值,以支持向量機分類器進行訓練與辨識。結果證明,本研究的提出的訊號分析處理能有效的提供在臨床上的思覺失調症病狀程度的參考資訊,而 MRI 處理流程也提供ㄧ些參考處資訊給予相關的研究者。
英文摘要 Patients with schizophrenia are typical mental disorder. Patients suffer from symptoms such as hallucination and delusion and, the society also need to burden highly financial cost for its treatment at the same time. Currently, there are many publications that discussed the treatment, diagnosis, prognosis, and damage in schizophrenia. Among them, the electroencephalography (EEG) is an important tool that can be used to be a functional neurological examination to diagnose brain dysfunction caused by non-structural brain lesions and magnetic resonance imaging (MRI) is a non-invasive research and analysis tool used by many experts and researchers to obtain images within the brain. Our findings could lead to the development of parameters or biomarkers for psychiatric symptoms in the magnetic resonance imaging study. We collected schizophrenia patients and normal controls groups by EEG and MRI. For the part of the EEG, the International Affective Picture System (IAPS) was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency and entropy were used to identify normal, moderately and markedly ill schizophrenic patients. For the part of the MRI, we used VBM to separate brain images to grey matter and white matter than mapping to Montreal Neurological Institute (MNI) space; After that, calibration images by DARTEL. Adoption joint source-based morphometry (jICA) to analysis of two groups in the grey matter and white matter images. Finally, we used brain structure images and the error rate of the Wisconsin Card Sorting Test (WCST) as features in support vector machine (SVM) learning and classification. The results indicate that the signal analysis method proposed in this study can provide reference information that can be used to determine the phases of schizophrenia symptoms in clinical applications.
論文目次 CHAPTER 1. INTRODUCTION 1
1.1. BACKGROUND 1
1.2. LITERATURE REVIEW 3
1.2.1. Application of EEG in Schizophrenia 3
1.2.2. Application of MRI in Schizophrenia 5
1.2.3. Classification 7
1.3. FLOW CHART OF EXPERIMENTS 8
CHAPTER 2. MATERIAL AND METHODS 9
2.1. PARTICIPANTS 9
2.2. MATERIALS AND EXPERIMENTAL SETUP 13
2.3. DATA ANALYSIS 16
2.3.1. EEG Signal Processing 16
2.3.2. Entropy Calculation Method 19
2.3.3. MRI Data Analysis 22
2.3.4. Classification SVM 25


CHAPTER 3. RESULTS 26
3.1. THE RESULTS FOR EEG 26
3.1.1. Classification of different groups based on brainwaves 26
3.1.2. Correlation analysis between the features of each frequency bands and the total scores of PANSS 28
3.1.3. Independent-samples t test between the features of beta frequency bands and the group 2&3 28
3.2. THE RESULTS FOR MRI T1 IMAGES 31
3.2.1. Joint ICA results 31
3.2.2. Classification Results 32
CHAPTER 4. DISCUSSION 33
CHAPTER 5. CONCLUSIONS AND FUTURE WORKS 38
REFERENCE 39
APPENDIX 53


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