對於現代醫學來說，透過腦電圖(Electroencephalography, EEG)來作為腦部疾病診斷是一種十分常見的方式，例如癲癇、思覺失調、睡眠障礙等與腦、心理學相關的領域都有著廣泛的應用。回顧過去關於癲癇疾病的相關研究文獻，往往需要大量的歷史資料量以及計算量才能使疾病在分類上有較明顯的效果。
有鑑於此，本研究提出一種適用於腦電圖的輕量化分類演算法，稱為Permutation-ratio Entropy of Entropy (PREoE)。利用腦電圖與心電圖相似的生物獨特性，其精神是基於心電圖(Electrocardiography, ECG)輕量化分類演算法(Entropy of Entropy, EoE)，並結合適用於腦電圖分析的分類演算法(Permutation-ratio Entropy, PRE)來考量腦電圖資料在時序上的關聯性而成。實驗以癲癇(Epilepsy)的腦電圖分類為例，結果顯示本研究不僅能有效減少所需的資料量以及計算量，並能維持一定的分類準度。

Nowadays, Electroencephalography (EEG) has been widely used in psychology and diagnosing brain diseases, such as epilepsy, schizophrenia, sleep disorders, and so on. Reviewing those related researches on epilepsy in the past requires a large amount of historical data and calculation to make the disease have a more obvious classification effect.
Given that, this research proposes the Permutation-ratio Entropy of Entropy (PREoE), a lightweight classification scheme suitable for EEG. According to the similarity in both Electrocardiography (ECG) and EEG, PREoE is based on a lightweight classification algorithm for ECG called Entropy of Entropy (EoE). Also, considering the patterns among EEG signals, we combined a useful EEG classification algorithm so-called Permutation-ratio Entropy (PRE) into our research. In the experiment, epilepsy EEG signals were used. The results indicate that PREoE not only reduces the amount of required data and calculations but also maintains an available accuracy on classification.

表 次 vi
圖 次 vii
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第貳章 文獻探討 5
第一節 癲癇與熵的基本介紹 5
第二節 熵的腦電圖分析方法回顧 9
第參章 研究方法 15
第一節 研究概念 15
第二節 Entropy of Entropy (EoE) 之概念及公式 17
第三節 Permutation-ratio entropy (PRE) 之概念與公式 22
第四節 模型設計Permutation-ratio Entropy of Entropy (PREoE) 27
第肆章 實驗設計與結果 37
第一節 實驗設計 37
第二節 實驗結果 42
第伍章 結論 47
第一節 實驗結果總述與貢獻 47
第二節 研究限制與未來展望 49
參考文獻 50

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