跳到主要內容

臺灣博碩士論文加值系統

(3.236.110.106) 您好!臺灣時間:2021/07/29 16:49
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

我願授權國圖
: 
twitterline
研究生:鄭宇翔
研究生(外文):Yu-shian Cheng
論文名稱:法則式自動睡眠判讀方法
論文名稱(外文):A rule-based automatic sleep staging method
指導教授:梁勝富梁勝富引用關係
指導教授(外文):Sheng-fu Liang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:53
中文關鍵詞:法則式自動睡眠判讀
外文關鍵詞:automatic sleep stagingrule-based
相關次數:
  • 被引用被引用:0
  • 點閱點閱:133
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
人一天需睡眠約7~8小時,換句話說,睡眠在人們一天的時間中約占了1/3。在這麼長時間的過程中,大腦活動並不是完全靜止不動的,相反地,大腦會進行一連串不同階段的活動,且在不同的階段會產生一些不同型態的訊號;據此,人們將睡眠分為兩大類-非快速眼動期(NREM)ˋ快速眼動期(REM),而非快速眼動期中由淺至深可再分為淺睡期1(s1)ˋ淺睡期2(s2)ˋ熟睡期(SWS)。另一方面,大腦在不同階段所進行的一些活動經研究發現和學習或是疾病有關,像是s2和記憶學習有顯著的相關性;這些研究說明了判斷睡眠週期的重要性,但是,若是由人工判斷的話,會很耗時ˋ耗力,所以,本篇論文的目標就是提出一個準確率高且值得信賴的自動睡眠週期判讀的方法。
在這篇論文中,從兩台儀器中各收集了10筆共20筆的正常人的記錄,且經由對訊號和頻譜的分析,提出了一個高準確率(88.7% , 87.9%)且可靠的自動睡眠週期判讀方法。為了降低個體差異對自動睡眠週期判斷的影響,在分類之前,會先將運算好的特徵值(feature)作正規劃的動作,之後,再根據階層式的決策樹作判斷。決策樹最後判斷出來的結果總共分成14類,其中,清醒期佔1類,s1佔6類,s2佔4類,SWS佔1類,REM佔2類。週期的判斷是由結果所屬的週期來決定,ex.判斷出來的結果屬於s2的其中一類,則將目前週期判為s2。最後,根據睡眠週期的連續性和一些其他特性,考慮前後週期,將目前判斷的睡眠週期結果做調整,則得到最後判讀的睡眠週期結果。
People need to sleep about 7~8 hours each day. In other words, sleep takes about the 1/3 of the time one day. In such long time, the activation of the brain doesn’t stop. Contrarily, the brain carries through a series of multi-stepped and progressive activities, and produces some different types of signal in different step. Based on this, people divide the sleep into two clusters : non-rapid-eye-movement sleep (NREM), rapid-eye-movement sleep (REM), and NREM from light to deep is divided as s1, s2, SWS. On the other hand, it is found from the research that activities in different step have relation to the learning or diseases, like s2 have strong relation to memory learning. These researches prove the importance of separating sleep stages. But if doing by human beings, it consumes time and energy. Therefore, the aim of the paper is to propose a high accuracy and reliable automatic sleep staging method.
In this paper, we collect 10 records of normal subjects from each device of two, totally 20 records. Based on the analysis of the signal and the spectrum, we propose a high accuracy (88.8 % , 87.9%) and reliable automatic sleep staging method. For decreasing the effect of the individual difference on staging, we do normalization on features before the classification, and then make a decision by the hierarchical decision tree. The final result of the decision tree consists of 14 rules. Among 14 rules, wake holds 1, s1 holds 6, s2 holds 4, SWS holds 1, and REM holds 2. The staging is decided by the stage that the type of the result belongs to. For example, if the staging result is one of the types that belong to s2, the present epoch is staged as s2. Finally, according to the continuity and some other restrictions of the sleep stage, we consider the temporal contextual information and make some modifications on the proceeding results of sleep staging, getting the final answer of the automatic sleep staging.
Chapter 1 Introduction 6
1.1 Background 6
1.2 Polysomnography 7
1.2.1 Electroencephalogram (EEG) 7
1.2.2 Electrooculogram (EOG) 9
1.2.3 Electromyogram (EMG) 10
1.3 Manual staging rules of sleep 10
1.4 Movement stage 14
1.5 History of automatic sleep staging 14
1.6 Motivation 15
1.7 Thesis Organization 16
Chapter 2 Methods 17
2.1 Subjects 17
2.2 Recordings 17
2.3 Automatic sleep staging system 19
2.3.1 Pre-processing 20
2.3.2 Processing 21
2.3.2.1 Feature extraction and analysis 21
2.3.2.2 Normalization 27
2.3.2.3 Separation of movement stages 27
2.3.2.4 Classification (The proposed decision tree) 28
2.3.3 Post-processing 34
Chapter 3 Results 37
Chapter 4 Discussions 45
Chapter 5 Conclusions and future work 50
Reference 51
[1] Rechtschaffen A. and Kales A., Eds., “A Manual of Standardized Terminology, Techniques and Scoring System for Sleep stages of Human Subjects,” Brain Inform. Service/Brain Res. Inst., Univ. California, Los Angeles, 1968.

[2] Malmivuo J. and Plonsey R., “Principles and Applications of Bioelectric and Biomagnetic Fields,” Bioelectromagnetism, Oxford University Press, New York, 1995.

[3] Penhaker M., Imramovsky M., Tiefenbach P., Kobza F., Lékařské diagnostické přístroje : Učební texty. Ostrava : VŠB-TU Ostrava, 2004.

[4] Fejtova M. and Fejt J., “Eye as a new computer periphery,” Lékař a technika, 36: 45-50, 2006.

[5] Dement W. and Kleitmann N., “The relation of eye movement duringsleep to dream activity; an objective method for the study of dreaming,”J. Exp. Psychol., 55: 339-346, 1957.

[6] Smith J., Negin M., and Nevis A. H., “Automatic analysis of sleep electroencephalo- grams by hybrid computation,” IEEE Trans. Syst. Sci. Cybern., SSC-5: 278-284, 1969.

[7] Smith J. D. and Karacan I., “EEG sleep stage scoring by an automatic hybrid system,” Electroencephalogr. Clin. Neurophysiol., 31: 231-237, 1971.

[8] Martin W. B., Johnson L. C., Viglione S. S., Joseph P. N. R. D., and Moses J.D., “Pattern recognition of EEG-EOG as a technique for all-night sleep stage scoring,” Electroencephalogr. Clin. Neurophysiol., 32: 417-427, 1972.

[9] Stanus E., Lacroix B., Kerkhofs M., and Mendlewicz J., “Automatedsleep scoring: A comparative reliability study of algorithms,” Electroencephalogr. Clin. Neurophysiol., 66: 448-456, 1987.

[10] Kuwahara H., Higashi H., Mizuki Y., Tanaka S. M. M., and Inanaga K., “Automatic real-time analysis of human sleep stages by an interval histogram method,” Electroencephalogr. Clin. Neurophysiol., 70: 220-229, 1988.

[11] Ray S. R., Lee W. D., Morgan C. D., and Airth-Kindree W., “Computer sleep stage scoring-an expert system approach,” Int. J. Biomed. Computing, 19: 43-61, 1986.

[12] Schaltenbrand N. et al., “Sleep stage scoring using the neural network model: Comparison between visual and automatic analysis in normal subjects and patients,” Sleep, 19: 26-35, 1996.

[13] Agarwal R. and Gotman J., “Computer-assisted sleep staging,” IEEE Trans Biomed Eng, 48: 1412-23, 2001.

[14] Berthomier C., Prado J., Benoit O., “Automatic sleep EEG analysis using lter banks,” Biomed Sci Instrum, 35: 241-6, 1999.

[15] Duman F., Erdamar A., Erogul O., Telatar Z., Yetkin Sinan., “Efcient sleep spindle detection algorithm with decision tree,” Expert Systems with Applications, 36: 9980-9985, 2009.

[16] Park H., Park K., and Jeong D.U., “Hybrid neural-network and rule-based expert
system for automatic sleep stage scoring,” Proceedings of the 22nd Annual EMBS Inter-
national Conference, July 2000.

[17] Berthomier C., et al., “Automatic analysis of single-channel sleep EEG: validation in healthy individuals,” Sleep, 30: 1587-1595, 2007.

[18] Virkkala J., Hasan J., Värri A., Himanen S., and Müller K., “Automatic sleep stage classication using two-channel electro-oculography,” J. Neurosci. Methods, 166:109-15, 2007.

[19] Silber et al., “The visual scoring of sleep in adults,” J. Clin. Sleep Med., 3: 121-131, 2007.

[20] Penzel T. and Conradt R., “Computer based sleep recording and analysis,” Sleep Medicine Reviews, 4: 131-148, 2000.

[21] Steffen G., Matthias M., Kay H., and Jan B., “Learning-Dependent Increases in Sleep Spindle Density,” The Journal of Neuroscience, 22: 6830-6834, 2002.
[22] Stuart M.F. and Carlyle T.S., “Learning-dependent changes in sleep spindles and Stage 2 sleep,” J. Sleep Res., 15: 250-255, 2006.

[23] Schabus M. et al., “Sleep spindle-related activity in the human EEG and its
relation to general cognitive and learning abilities,” European Journal of Neuroscience, 23: 1738-1746, 2006.

[24] Kevin R.P., Laura R., Valerie S., and Carlyle S., “Changes in the density of stage 2 sleep spindles following motor learning in young and older adults,” J. Sleep Res., 17: 23-33, 2008.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top