睡眠研究的中心課題之一就是睡眠深度。根據R&K法則的定義，人類的睡眠階段主要區分為三種深度：清醒期、非快速動眼睡眠期及快速動眼睡眠期。而睡眠之分期主要係依據腦電圖，並輔以眼電圖及肌電圖進行判斷。
　　研究指出，許多睡眠期發生的疾病或睡眠障礙均會影響患者的生活品質。例如快速動眼睡眠期異常與神經系統退化疾病、精神性疾病如憂鬱症等存在有高度相關性；而睡眠呼吸暫止症為目前常見之睡眠障礙之一，其後遺症會對人體造成精神及心血管方面不良的影響…等。
　　本研究主要著眼於快速動眼睡眠期的偵測。考慮到居家應用環境，本研究僅使用眼電圖訊號進行分析，相較於腦電圖訊號，除了貼片相對容易固定，位置不易受睡姿影響外，訊號線也較少。透過觀察不同階段下眼電圖訊號的基本型態，利用向量量化編碼的技術，本研究發展的方法在群體應用有67.71%的分類成功率，靈敏度為73.38%，特異度為68.95%。而個人化應用下分類成功率均值為82.02%，靈敏度均值為83.05%，特異度均值為81.62%。顯示本方法用來偵測快速動眼睡眠期確有可行性，尤其是在個人化應用上，將有利於爾後進行個人睡眠狀態的長期追蹤研究。

One primary topic of sleep studies is the depth of sleep. According to definitions of R&K rules, human sleep can be roughly divided into three different stages: Awake, Non-rapid-eye-movement (NREM) Sleep, and Rapid-eye-movement (REM) Sleep. Moreover, sleep stages are scored mainly by EEG signals and complementally by EOG and EMG signals.
Many researchers have indicated that diseases or disorders occur during sleep will affect life quality of patients. For example, REM sleep-related dyssomnia is highly correlated with neurodegenerative or mental disorders such as major depression. Furthermore, sleep apnea is one of the most common sleep disorders at present. Untreated sleep apnea can increase the risk of mental and cardiovascular diseases.
This research proposes a detection method of REM sleep. Take into account the environment of homecare, we just extract and analyze EOG signals for the sake of convenience in comparison with EEG channels. By analyzing elementary waveforms of EOG signals based on VQ method, the proposed method performs a classification accuracy of 67.71% in a group application. The corresponding sensitivity and specificity are 73.38% and 68.95% respectively. In contrast, the average classification accuracy is 82.02% in personalized applications. And the corresponding average sensitivity and specificity are 83.05% and 81.62% respectively. Experimental results demonstrate the feasibility of detecting REM sleep via the proposed method, especially in personalized applications. This will be propitious to a long term tracing and research of personal sleep status.

目錄 i
表目錄 iii
圖目錄 v
摘要 viii
Abstract ix
第一章 緒論 1
1.1 引言 1
1.2 快速動眼睡眠與非快速動眼睡眠 1
1.3 睡眠與年齡 3
1.4 睡眠檢查 4
1.5 睡眠期的眼電圖訊號 6
第二章 研究目的 13
2.1 文獻回顧 13
2.2 清醒期、NREM睡眠期與REM睡眠期的眼電圖型態 15
2.3 研究動機與目的 18
第三章 向量量化編碼方法 21
3.1 最近鄰居分類器 21
3.1.1 基本原理 21
3.2 向量量化原理 23
3.3 傳統LBG演算法 25
3.3.1 向量量化過程中的失真 25
3.3.2 傳統LBG新增代碼方法 26
3.4 修正型LBG演算法 27
3.4.1 主軸方向 27
3.4.2 修正型LBG演算法 28
第四章 快速動眼睡眠期的偵測方法 32
4.1 應用VQ方法於眼電圖訊號 33
4.2 代碼的重新編序 39
4.3 代碼的兩兩組合 44
4.4 資料內容重新編排 48
第五章 實驗結果與討論 52
5.1 20人的群體試驗 56
5.2 20人次的單人試驗 64
5.3 20位受測者的REM期睡眠結構圖 67
第六章 結論與未來展望 81
參考文獻 84
附錄I 分類器簡介 90
I.1 類神經網路基本架構 90
I.2 委員會機器（Committee Machine） 91
附錄II 分類器之訓練精度列表 93

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