精神分裂症(Schizophrenia) 是一種慢性的腦部疾病，全球約有1%的人口患有此疾病，而15至45歲的年輕人口為較易發的族群。一般來說，由於精神分裂症患者對於真實與幻覺的區別是有障礙的，因此對他們在社會適應及職場表現有嚴重的影響。迄今，精神分裂症的臨床診斷主要是基於患者的行為紀錄及其心理精神狀態評估 (DSM-V)。 此篇論文的目的為透過客觀的生理的資訊進行精神分裂症患者與一般健康者的分類，並建立有效率的分類系統。本研究的受測者分為兩個族群：接受治療中的十二位精神分裂症患者和十二位一般健康者。在聽覺事件相關電位的實驗中，隨機播放和弦刺激與音程刺激，藉由觀察大腦各電極事件相關電位中N1與P2的統計結果，將有統計差異的電極進行不同組合；並將N1與P2的振幅作為特徵，結果發現一些重要的電極位置；其相關大腦位置位於前額葉與顳葉，這些大腦腦區與精神分裂症患者的腦部功能缺陷可能存有相關性。測試結果顯示，組合具有統計差異的特定電極 (F3, F4, T3; F3, F7, T4; F3, F8, T4 三種組合方式) 被和絃刺激與音程刺激所誘發的N1振幅作為特徵時，並採用Leave-One-Out交叉驗證與LDA線性分類器，可達到91.67%之準確率。結果顯示在特定電極位置的N1振幅可作為有效特徵於區別精神分裂症患者與一般健康者。本研究利用統計方法選取有鑑別力之電極位置與特徵，結合機器學習，發展出可有效分辨精神分裂症患者及一般健康者之系統。此系統除了提供分類的高準確率，亦透過分類結果提供未來臨床診斷與更深入的研究相關資訊做為參考。

Schizophrenia is a chronic brain disorder. 1% population worldwide have this illness approximately and it typically occurred in young adulthood (15~45 year). Generally, schizophrenia patients usually could not identify reality and hallucination and it would make social and occupational dysfunctions. Until now, diagnosis of schizophrenia is based on observed behavior and the patient's reported experiences (Diagnostic and Statistical Manual of Mental Disorders, DSM-V).
The aim of this study is to evaluate the feasibility of classifying schizophrenia and healthy people by analyzing their objective physiological information, and construct an efficient classification procedure. In this thesis, the auditory even-related potentials (AEP) experiment were recorded from 12 schizophrenia patients and 12 healthy people. The stimuli was presented randomly by chord stimuli and interval stimuli, by observing the statistical difference of ERPs results in N1 and P2 components of each musical stimuli type in each group and selected the discrepant electrode sites for different combination, the amplitude of N1 and P2 of the specific electrode sites were taken as features for classification, and some specific electrode sites were found to be important through the classification result. The related locations of those electrode sites are roughly at frontal and temporal lobes in brain, which may be connected with brain functions impairment in Schizophrenia. Finally, the extracted features were fed into the LDA for classification. Accuracy of the proposed method can reach 91.67% through leave-one-out cross validation when combining N1 amplitude at statistically discrepant electrode sites (i.e. F3, F4, T3; F3, F7, T4; F3, F8, T4 three combination modes) as feature. The result showed that N1 amplitude of specific electrode sites supply useful features in Schizophrenia classification. In this thesis, statistical techniques combined with machine learning were used to select discriminating electrode sites and features for classification, and to develop an efficient and effective classification system. Furthermore, information observed through the classification result can be provided for the clinical diagnosis and deeper analysis in the future.

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