本研究著重在各階段睡眠分類上的問題，依據每階段所要判斷的項
目，設計出專門的特徵向量變數。分類總共分為四個階段，第一階段判讀
出慢波睡眠(Slow Wave Sleep)，第二階段判斷出清醒期(Wake)，第三階段判
斷出快速動眼期(REM)，第四階段則是區分第二階段(Stage2)和第一階段
(Stage1)。
在訊號上則是使用眼動圖訊號，每頁30 秒下求取特徵變數，並經由倒
傳遞類神經網路訓練出各階段的分類器加以分類。在靈敏度(Sensitivity)以
及陽性預測律(Positive Predictive Value)除了第一階段(Stage1)，其餘皆在
70-80%。總體精度達到74.80%。

This study aims at sleep stage classification problem via EOG signals.
The classification problem consists of four steps. The first step is to
distinguish slow wave sleep from the rest of the sleep periods. Wake periods
are identified in the second step. The third step finds REM sleep and the last
step classifies stage 2 and stage1 sleep.
By using different EOG signal features in different steps of the
classification process, this work uses back-propagation trained neural
networks to perform classification.
With the exception of stage 1 sleep, the sensitivity and positive
predictive value ranges from 70% to 80%. The overall classification accuracy
is 74.80%.

目錄…....................................................................................................................I
圖目錄………………………………………………………………………..IV
表目錄………………………………………………………………………VII
摘要…….…………………………………………………………………….VIII
Abstact ………………………………………………………………………..IX
第一章緒論......................................................................................................... 1
1.1. 前言........................................................................................................ 1
1.2. 研究動機與目的.................................................................................... 2
1.3. 論文架構................................................................................................ 3
第二章睡眠分期與眼動圖訊號......................................................................... 4
2.1 睡眠檢查................................................................................................ 4
2.2 眼動圖的訊號........................................................................................ 6
2.3 睡眠週期與狀態.................................................................................... 9
2.4 睡眠分期規則...................................................................................... 10
2.5 各階段睡眠期眼動圖訊號.................................................................. 11
第三章分類器架構與特徵演算法................................................................... 15
3.1 類神經網路.......................................................................................... 15
II
3.2 委員會機器.......................................................................................... 17
3.3 最近鄰居分類器.................................................................................. 18
3.1.1 原理......................................................................................... 19
3.4 向量量化編碼方法.............................................................................. 19
3.4.1 向量資料量化原理................................................................. 20
3.5 LBG 演算法......................................................................................... 21
3.6 應變式VQ 分類方法.......................................................................... 22
3.7 Simplex 演算法................................................................................... 24
第四章以眼動圖訊號處理睡眠階段分類問題............................................... 31
4.1 建立眼動圖的特徵訊號...................................................................... 31
4.1.1 直方圖特徵............................................................................. 31
4.1.2 越零點數目............................................................................. 35
4.1.3 能量百分比............................................................................. 37
4.1.4 頻帶能量(Band Power) ........................................................... 37
4.1.5 Lempel-Ziv Complexity .......................................................... 39
4.1.6 越零點面積加權..................................................................... 42
4.2 建立第一階段分類器.......................................................................... 43
4.3 建立第二階段分類器.......................................................................... 45
4.4 建立第三階段分類器.......................................................................... 47
III
4.5 門檻值法則.......................................................................................... 49
4.6 建立第四階段分類器.......................................................................... 50
4.7 建立鄰居法則分類器.......................................................................... 52
4.8 所有睡眠階段判讀.............................................................................. 52
第五章實驗結果與討論................................................................................... 54
5.1 分類器效能.......................................................................................... 54
5.2 總分類結果.......................................................................................... 58
第六章結論....................................................................................................... 64
參考文獻............................................................................................................. 65
附錄I 艾普渥斯嗜睡度量表(Epworth sleepiness scale, ESS)......................... 67

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