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系統識別號 U0026-0511201805011400
論文名稱(中文) 基於低工作負荷非侵入性測量的思覺失調症候群輔助診斷技術
論文名稱(英文) Auxiliary diagnosis of Schizophrenic Patients Based on Low-workload and Non-invasive Measurement
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 107
學期 1
出版年 107
研究生(中文) 謝宗澔
研究生(英文) Tsung-Hao Hsieh
學號 P78991189
學位類別 博士
語文別 英文
論文頁數 65頁
口試委員 指導教授-梁勝富
口試委員-蕭富仁
口試委員-王淵弘
召集委員-龔俊嘉
口試委員-郭至恩
中文關鍵字 思覺失調症候群  腦電圖  功能性磁振造影  聽覺相關事件電位  靜息態  神經網絡複雜度  區域同質性 
英文關鍵字 Schizophrenia  electroencephalography  functional Magnetic Resonance Imaging  auditory-related event potentials  resting states  brain network complexity  regional homogeneity 
學科別分類
中文摘要 思覺失調症候群(Schizophrenia, Sz)是現代一項廣為人知的精神疾病。全世界約每100人就1人患病,好發於18-25歲青春期,並且有超過40%的機率形成終身性疾病,影響患者的整個人生。根據中華民國衛生福利部統計,思覺失調症候群於2017年首次成為十大健保成本高昂的疾病,年花約127億新台幣,顯示國人對該疾病的診療需求日益提高。思覺失調症候群主要的病徵是幻聽、幻視等知覺與思維上的功能性障礙,患者無法區分現實與幻覺,從而影響情感與行為等內外表徵,而造成各種社會適應障礙。但是,造成思覺失調症候群的成因尚未明朗,在病情的診斷上仰賴病人自己或是親友的將主觀意見告訴醫生,看有沒有符合主要的病癥。根據精神疾病診斷與統計手冊第五版(The Diagnostic and Statistical Manual of Mental Disorders, DSM)所敘述的病徵診斷流程,可能需要1-6個月以上的長期追蹤,才能確診患病與否。另外,弔詭的是,由於病人無法區分現實與幻覺,難以發覺自身異常就診,多由親友或社工觀察到異常行為才通報。這可能錯失早期治療機會形成終身性疾病。因此,本研究的目的是通過分析生理信息,提出一種客觀區分思覺失調症候群識別方法,用於初期篩檢的輔助診斷資訊,用以減低醫護人力的消耗以及把握早期治療的機會。所選用的生理信息有兩個特點(1)易於實行且儘量不造成受試者不適(2)除提供輔助診斷外,該生理訊息也可做為醫師深入診斷的參考。

本文選用了兩種非侵入量測的生理信息-腦電波(時間解析度高)與功能性磁振造影(空間解析度高);腦電波採用Liang等人在2013提出以被動式聆聽音樂刺激所誘發的聽覺相關電位(Auditory event related potential, AEP),受試者不需要進行繁複的任務操作,只需要閉眼被動聆聽不同複雜程度的聲音刺激。思覺失調症候群患者在面對複雜度高的聲音刺激時,所誘發的AEP顯著小於正常人。研究中分析了AEP得N1與P2做為特徵,利用資訊熵與相關性做特徵選取策略。經由12位正常人與12位思覺失調症患者所建立的線性分類模型(LDA),在留一交叉測試中(leave-one-out)分類準確度達到83.33%;另一方面,功能性磁振造影也是一項方便的非侵入量測方式,但量測過程中伴隨噪音,故不採用前述的AEP刺激任務,而是評估受試者靜息態下的大腦網路連結差異,受試者不需要進行任務只需要放鬆靜待5-10分鐘的掃描完成。我們分析了公開資料庫COBORE所提供的72位正常人與69位思覺失調症患者的靜息態功能性磁振造影,思覺失調症患者的全腦網路複雜度與區域同質性均低於一般人,最終我們利用全腦網路複雜度所提出的線性支援向量分類模型(linear-SVM),在留一交叉測試中(leave-one-out)分類準確度達到73.05%。
無論腦電波或是功能性磁振造影兩者的正確率都達到70%以上,並且實驗方法簡單易於實行並不帶給受試者太大困擾。兩者所提供的全腦分析資訊有望成為一種有用的工具幫助我們了解腦功能的異常和思覺失調症的診斷以及治療方向。同時,我們發現兩種生理訊號都顯示思覺失調症患者在前額葉以及顳葉均有異常的功能障礙,這有助於我們更進一步簡化AEP實驗以及針對區域開發可攜式的診斷裝置。
英文摘要 Schizophrenia (SZ) is a well-known and one of the least understood and costliest mental disorders in terms of human suffering and societal costs, and it occurs in about 1% of the general world population. The first-episode of this neuropsychiatric mostly occurs in 18-25 years old, and has over 40% chance to become a lifetime illness that affects the patient's entire life. According to the statistics of the Ministry of Health and Welfare of the R.O.C, schizophrenia first time became the top ten cost of national health insurance in 2017. Each year, government need pay about $12.7 billion NTD in patient’s diagnosis and treatment. The main symptoms are hallucinations or delusions of perception and cognitive, patient can identify the reality and hallucinations, and then affect their outside characterization such as behavior, language and emotion. However, the causes of schizophrenia are not yet clear, current diagnosis is a time-consuming work which rely on the subjective report of patient-self or its relative person. According to The Diagnostic and Statistical Manual of Mental Disorders (DSM), it may take more than 1-6 months to follow-up and confirm the diagnosis. Paradoxically, it is difficult to ask patient self-report their first-episode because they cannot identify the reality and hallucinations. Usually this part relies on relative person observe their abnormal behavior, language and emotion then reported. It may become a lifetime illness. The aim of this study is to propose the objectively distinguishing methods for identification Schizophrenia by analyzing physiological information. It will help in reducing the time course of the diagnosis and provides additional information to the doctor. The chosen measure of physiological information should be having two characteristics (1) easy to implement and not to cause discomfort to the subject; (2) In addition to auxiliary diagnosis, the physiological information can also provide diagnosis or treatment information.
In this paper, two non-invasive measurements of physiological information – Electroencephalography (EEG, high-resolution in temporality) and functional magnetic resonance imaging (fMRI, high-resolution in spatiality) were selected; For EEG measurement, we refer the procedure of Liang et al (2013) to evoke the auditory-related potential (AEP). In this procedure, the subject does not need to perform any task, just close the eyes and passively listen to the sound stimulation of different complexity. The patient's AEP amplitude will be significantly smaller than normal. Further, a feature selection strategy combines discrimination and correlation analysis is also proposed to select key features and remove redundancy. Two AEP components, amplitude of N1 evoked by chord stimuli and amplitude of P2 evoked by interval stimuli from the frontal lobe, were screened and fed to the linear discriminate analysis (LDA) for classification. The accuracy reaches 83.33% through leave-one-out cross-validation from 12 SZ and 12 healthy subjects; on the other hand, fMRI is also a convenient non-invasive measurement. However, the environmental noise let AEP procedure cannot function. Hence, we analyzed the resting state fMRI of 72 normal people and 69 patients with mental disorders from the public database - COBORE. Durning rs-fMRI recording, participants are typically asked to rest quietly with their eyes open or closed for 5-10 minutes and without performing any tasks. The whole brain network complexity and regional homogeneity of patients were significant lower than normal people. Accuracy of the proposed linear-SVM can reach 73.05% through leave-one-out cross validation.
Both our proposed measurements provide over 70% correction, and the recording methods are simple and easy to implement. More important, the measurements do not bring workload or strong discomfort to subject. Moreover, both measurements were point out the frontal lobe and temporal lobe were the key role of dysfunctional in schizophrenia. It is expected to be a useful tool to help us understand abnormalities of brain function and a potential biomarker to plane the treatment strategy in schizophrenia. Moreover, it has high potential in simplifying the AEP procedure and develop portable diagnostic devices for the specific region.
論文目次 Contents
Abstract in chinese I
Abstract in english III
誌 謝 V
Chapter 1 Introduction 3
1. 1. Schizophrenia 3
1. 2. EEG in Schizophrenia 5
1. 3. fMRI in Schizophrenia 6
1. 4. Classification in Schizophrenia 8
1. 5. Motivation and Objective 10
Chapter 2 Identification of Schizophrenic Patients Based on Functional Abnormalities in The cortical processing of Sound Complexity and Musical Consonance 12
2. 1. EEG Recording and Methods 12
2. 1.1 Subjects 12
2. 1.2 Stimulus 13
2. 1.3 Procedure 16
2. 1.4 Schizophrenia Classification System 17
2. 2. Result 24
2. 2.1 AEPs analysis 24
2. 2.2 Feature selections 27
2. 2. 3 Classification performance 28
2. 3. Discussion 32
Chapter 3 Diagnosis of Schizophrenia patients based on brain local connectivity and network complexity analysis of resting-state fMRI 36
3. 1. Materials 36
3. 1.1 Dataset 36
3. 1.2 Data preprocessing 37
3. 2. Method 37
3. 2.1 Local regional connections- Regional Homogeneity 38
3. 2.2 The complexity of whole brain network 41
3. 2.3 Classification model 44
3. 3. Result 45
3. 3.1 Classification performance 45
3. 3.2 Analysis of network correlation and complexity 47
3. 3.3 Analysis of local regional connections- Regional Homogeneity 49
3. 4. Discussion 51
Chapter 4 Conclusions and Future Work 54
Reference 56
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