精神分裂症(Schizophrenia)是一種嚴重且慢性的腦部疾病，影響全世界大約1%的人口，且好發於年輕的成年人口。常見的症狀包含幻覺、妄想、社交能力喪失和缺乏情緒起伏等。目前精神分裂症的診斷方式主要是基於觀察患者長達六個月的行為紀錄及其心理精神狀態評估(DSM)。過去的研究顯示聽覺感知已被用來檢驗神經系統理論和發展評估疾病系統，例如音樂家的神經可塑性和精神分裂症患者對於聽覺處理的異常。在過去的研究中，我們發現一般健康者、音樂家和精神分裂患者在大腦聽覺事件相關電位有明顯的差異。因此，本篇論文的目的為透過客觀的電生理資訊來進行一般健康者、音樂家和精神分裂患者的分類。本研究所建立的分類策略不只可以建立一個有效且客觀的分類系統來輔助診斷精神分裂症患者，也能用來區分一般人和聽覺感知特化的音樂家。
本研究的受測者分為三個族群：十二位受過專業訓練的音樂家、十二位接受治療中的精神分裂患者和十二位一般健康者。在聽覺事件相關電位的實驗中，隨機撥放三種和弦刺激與兩種音程刺激，藉由觀察大腦事件相關電位N1和P2的統計結果，選出有統計差異的特徵，並結合兩階段式分類策略選出最佳的特徵組合。分類結果顯示，當在第一階段先分出精神分裂患者，然後在第二階段分出音樂家和一般健康者，最好的整體準確率可達到91.67%。在精神分裂症的分類上，可達到97.22%的準確率；在音樂家的分類上，可達到91.67%的準確率。因此本篇論文提出的分類策略，不只可以正確分類精神分裂症患者，也可以正確分類音樂家。本研究利用統計方法選取有鑑別度的特徵，並且搭配兩階段式策略來分類聽覺感知程度不一樣的三個族群。除此之外，也提供一個有高準確率、有效且客觀的系統可做為臨床上的應用來輔助診斷精神分裂症患者。

Schizophrenia is a severe and chronic brain disorder that affects about 1% population worldwide and it typically occurs in young adulthood. Common symptoms include hallucinations, delusions, social withdrawal, reduced emotional expression, etc. Currently, diagnosis of schizophrenia is based on observing the patient's behavior and reported experiences for 6 months or more (Diagnostic and Statistical Manual of Mental Disorders, DSM). Past studies showed that auditory perception had long been used to test theories for neural system or develop assessment system for illness, such as neuroplasticity in musician’s brain and auditory processing deficits in schizophrenia patients. In previous studies, we found that difference in auditory event-related potentials (AEP) existed among normal subjects, musicians, and patients with schizophrenia. Therefore, the aim of this study is to classify normal subjects, musicians, and patients with schizophrenia by analyzing objective electrophysiological information. This study constructs a classification strategy which can not only develop an effective and objective classification system to help diagnosis of schizophrenia, but also distinguish musicians who have specialized auditory perception from normal people.
In this thesis, the auditory event-related potentials experiments were recorded from 12 well-trained musicians, 12 medicated schizophrenia patients and 12 normal subjects. The stimuli was presented randomly by three kinds of chord stimuli (major, diminish and atonal) and two kinds of interval stimuli (perfect fifth and tritone), by observing the statistical results of AEPs in N1 and P2 amplitude of each stimuli type in groups and selected which had statistical difference as features, and then combined with two-stage strategy to select best feature combinations for classification. Results show that when firstly separating schizophrenia patients from the others in stage one, and then separating musicians and normal subjects in stage two, the best overall accuracy can reach 91.67%. The accuracy for schizophrenia classification can achieve 97.22%; the accuracy for musician classification can achieve 91.67%. Hence, this thesis proposed a classification strategy which can not only correctly classify schizophrenia patients but also correctly classify musicians. Finally, this study classified three groups who had distinct levels of auditory perception by using discriminating features and two-stage hierarchical strategy. Furthermore, this also provided a high accuracy, effective and objective classification system for clinical utility to help diagnosis of schizophrenia.

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