睡眠結構是睡眠醫學其中一個重要指標，整夜的睡眠結構可分為清醒期、快速動眼期和非快速動眼期，其中非快速動眼期又可分為第一睡眠期、第二睡眠期和慢波睡眠期，其判讀的依據主要是根據腦電圖、眼電圖和肌電圖這三個頻道來作判讀。
本研究的目地是利用單頻道腦電波訊號進行自動睡眠階段判讀，其中分四個階段做為判讀的過程，第一階段分類慢波睡眠期與其它睡眠期，第二階段分類清醒期和第一睡眠期與快速動眼期和第二睡眠期，第三階段分類清醒期與第一睡眠期，第四階段分類快速動眼期與第二睡眠期，其測試者為62人，總epoch個數為48727個，自動睡眠階段判讀總體精度為76.98%，其中清醒期的靈敏度為85.96%，PPV為68.35%，快速動眼期的靈敏度為82.13%，PPV為74.11%，第一睡眠期的靈敏度為9.02%，PPV為39.00%，第二睡眠期的靈敏度為84.19%，PPV為79.36%，慢波睡眠期的靈敏度為81.53%，PPV為85.40%。

One of the important measures for sleep quailty is sleep structure. Normal sleep consists of awake, rapid eye movement (REM) sleep and nonrapid eye movement (NREM) sleep states. NREM sleep can be further classified into stage 1, stage 2 and slow wave sleep (SWS). These stages can be analyzed quantitatively from various electrical signals such as the electroencephalogram (EEG), electro-oculogram (EOG), and electromyogram (EMG).
The goal of this research is to develop a simple four-stage process to classify sleep into wake, REM, stage 1, stage 2 and SWS by using a single EEG channel. By applying the proposed approach to 48727 distinct epochs which are acquired from 62 persons, the experimental results show that the proposed method is achieves 76.98% of accuracy. The sensitivity and PPV for wake are 85.96% and 68.35%. Furthermore, the sensitivity and PPV for REM are 82.13% and 74.11%, respectively. The sensitivity and PPV for the stage 1 are 9.02% and 39.00%. The sensitivity and PPV for the stage 2 are 84.19% and 79.36%. The sensitivity and PPV for SWS are 81.53% and 85.40%.

摘要 .............................................................................................................................. V
Abstract ........................................................................................................................ VI
第一章 緒論 ................................................................................................................. 1
1.1 引言................................................................................................................. 1
1.2 研究動機與目的 .............................................. 2
第二章 睡眠醫學以及腦電波訊號 ...................................... 4
2.1 睡眠檢查 .................................................... 4
2.2 腦電圖生理電位及電極位置 .................................... 4
2.3 夜間睡眠及腦波 .............................................. 6
2.4 睡眠期以及其腦波特徵判讀標準 ............................... 10
第三章 分類器 ..................................................... 15
3.1 類神經網路 ................................................. 15
3.2 最近鄰居分類法 ............................................. 17
3.3 向量量化壓縮法 ............................................. 19
3.4 傳統型LBG................................................. 21
3.5 修正型LBG ................................................. 22
3.6 應變式VQ 分類法 ............................................ 25
第四章 以腦電圖進行睡眠階段判讀 ................................... 27
4.1 建立第一階段分類器 ......................................... 27
4.1.1 越零點及越零點直方圖特徵.............................. 28
4.1.2 越零點面積及越零點加權面積特徵........................ 34
4.1.3 頻帶能量特徵 .......................................... 36
4.1.4 Lempel-Ziv Complexity .................................. 37
4.1.5 實驗流程.............................................. 40
4.2 建立第二階段分類器 ......................................... 41
4.2.1 頻譜直方圖特徵........................................ 42
4.2.2 時域能量特徵.......................................... 42
4.2.3 實驗流程.............................................. 43
4.3 建立第三階段分類器 ......................................... 44
4.4 建立第四階段分類器 ......................................... 44
4.5 進行睡眠階段判讀 ........................................... 45
第五章 實驗結果與討論 ............................................. 47
5.1 分類評估的指標 ............................................. 47
5.2 各階段分類器訓練結果 ....................................... 49
5.3 各階段分類修補訓練結果 ..................................... 52
第六章 結論 ....................................................... 56
參考文獻 .......................................................... 58

Agarwal R and Gotman Jean. “Computer-Assisted Sleep Staging”, IEEE Transactions on Biomedical Engineering, vol.48, pp1412-1423, 2001.
Baumgart-Schmitt R , Herrmann W M et al. “On the Use of Neural Network Techniques to Analyze Sleep EEG Data”, Neuropsychobiology , vol. 37,pp49-58
Caffarel J, Gibson J et al. “Comparison of manual sleep staging with automated neural network-based analysis in clinical practice”, International Federation for Medical and Biological Engineering, vol 44,pp105-110, 2006.
Estrada E, Nazeran H et al. “EEG Feature Extraction For Classification Of Sleep Stages”,Proceedings of the 26th Annual International Conference of the IEEE EMBS ,pp196-199 ,2004.
Flexer A , Gruber G et al. “A reliable probabilistic sleep stager based on a single EEG signal”, Aritifiicial Intelligence in Medicine, vol.33,pp199-207, 2005.
Fell J, Röschke J et al, “Discrimination of sleep stages:a comparison between spectral and nonlinear EEG measure”, Electroencephalography and clinical Neurophysiology, vol 98, pp401-410, 1996.
Haykin S, 1999, Neoral Networks : A Comprehensive foundation, 2nd ed. Englewood cliffs , NJ: Pretice-Hall.
Iber C,MD et al. “The AASM Manual for the Scoring of Sleep and Associated Event”AASM Manual for Scoring Sleep, 2007.
Louis RP., Lee J et al, “Design and validation of a computer-based sleep-scoring algorithm”, Journal of Neuroscience Methods, vol 133, pp71-80, 2004.
Magosso E , Ursino M et al. “Visual and computer-based detection of slow eye movements in overnight and 24-h EOG recordings”, Clinical Neurophysiology, vol.118, pp1122-1133, 2007.
Park H J , Oh J S et al. “Automated Sleep Stage Scoring Using Hybrid Rule and Case-Based Reasoning”, Computers and Biomedical Reserach, vol.33, pp330-349, 2000.
Pinêroa P , Garciaa P , “Sleep stage classification using fuzzy sets and
machine learning techniques”, Neurocomputing, vol. 58–60, pp58–60, 2004.
Principe JC, Gala SK et al, “Sleep Staging Automaton Based on the Theory of Ecidence”, IEEE Transactions on biomedical Engineering, vol. 35, No.5, pp503-509, 1999.
Porèe F, Kachenoura A et al. “Blind Source Separation for Ambulatory
Sleep Recording”, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol. 10, No. 2, pp293-301, 2006.
Tian JY, Liu JQ “Automated Sleep Staging by a Hybrid System Comprising Neural Network and Fuzzy Rule-based Reasonong”, IEEE Engineering in Meddicine and Biology 27th Annual Conference, pp4115-4118, 2005.

楊傑能, 2007, “以眼電圖訊號檢測快速動眼睡眠期的向量量化編碼方法, ”博士論文, 國立中山大學機械與機電工程研究所.
吳炯廷, 2007, “以雙眼眼電圖相關係數偵測快速動眼睡眠期, ”碩士論文, 國立中山大學機械與機電工程研究所.
邱晧智, 2008, “以單頻道腦電波訊號偵測慢波睡眠, ”碩士論文, 國立中山大學機械與機電工程研究所.

劉勝義, 2004 , 臨床睡眠檢查學, 合計圖書出版社
葉怡成, 1997, 應用類神經網路, 儒林