(3.238.173.209) 您好!臺灣時間:2021/05/12 13:11
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:陳道信
研究生(外文):Tao-hsin Chen
論文名稱:以眼動圖訊號處理睡眠階段分類問題
論文名稱(外文):Using EOG Signals for Sleep Stage Classification
指導教授:嚴成文
指導教授(外文):Chen-Wen Yen
學位類別:碩士
校院名稱:國立中山大學
系所名稱:機械與機電工程學系研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:79
中文關鍵詞:眼動圖慢波睡眠
外文關鍵詞:Slow Wave SleepEOG
相關次數:
  • 被引用被引用:1
  • 點閱點閱:294
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:101
  • 收藏至我的研究室書目清單書目收藏:0
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
Virkkala Jussi, Hasan Joel, Värri Alpo, Himanen Sari-Leena, Müller Kiti,
2007,” Automatic sleep stage classification using two-channel
electro-oculography,” Journal of euroscience Methods, Vol. 166, pp.
109-115.
Virkkala Jussi, Hasan Joel, Värri Alpo, Himanen Sari-Leena, Müller Kiti,
2007,” Automatic detection of slow wave sleep using two channel
electro-oculography,” Journal of euroscience Methods, Vol. 160, pp.
171-177.
Park Hae-Jeong, Oh Jung-Su, Jeong Do-Un, Park Kwang-Suk, 2000,”
Automated Sleep Stage Scoring Using Hybrid Rule- and Case-Based
Reasoning,” Computers and Biomedical Research, Vol. 33, pp. 330-349.
Agarwal Rajeev, Takeuchi Tomoka, Laroche Suzie, Gotman Jean, 2005,”
Detection of Rapid-Eye Movements in Sleep Studies,” IEEE
TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol. 52, No. 8, pp.
1390-1396.
Caffarel Jennifer, Gibson G. John, Harrison J. Phil, Griffiths Clive J, Drinnan
Michael J, 2006,” Comparison of manual sleep staging with automated
neural network-based analysis in clinical practice,” Med Biol Eng Comput,
66
Vol. 44, pp. 105-110.
Louis Rhain P, Lee James, Stephenson Richard, 2004,” Design and validation of
a computer-based sleep-scoring algorithm,” Journal of Neuroscience
Methods, Vol. 133, pp. 71-80.
Tian J.Y., Liu J.Q., 2005,” Automated Sleep Staging by a Hybrid System
Comprising Neural Network and Fuzzy Rule-based Reasoning,”
Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th
Annual Conference, pp. 1-4.
Masaaki Hanaoka, Masaki Kobayashi, Haruaki Yamazaki, 2002,” Automatic
Sleep Stage Scoring Based on Waveform Recognition Method and
Decision-Tree Learning,” Systems and Computers in Japan, Vol. 33, No.
11, pp. 2672-2683.
Agarwal Rajeev, Gotman Jean, 2001,” Computer-Assisted Sleep Staging,” IEEE
TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol. 48, No. 12, pp.
1412-1423.
Huang Liyu, Sun Qixin, Cheng Jingzhi, 2003,” Novel Method of Fast
Automated Discrimination of Sleep Stages,” Proceedings of the 25th
Annual Intemational Conference of the IEEE EMBS, pp. 2273-2276.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔