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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉佩玲(Pei-Ling Liu) | |
| dc.contributor.author | Zhi-Yu Ruan | en |
| dc.contributor.author | 阮智宇 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:13:24Z | - |
| dc.date.available | 2015-08-26 | |
| dc.date.copyright | 2013-08-26 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-19 | |
| dc.identifier.citation | Acharya RU, Faust O, Kannathal N, Chua T, Laxminarayan S., “Non-linear analysis of EEG signals at various sleep stages,” Comput Meth Programs Biomed, 2005; 80: p. 37-45.
Chapotot, F., Becq, G., “Automated sleep-wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules,” Int J Adapt Control Signal Process 2010; 24: p. 409-23. Costa, M., Goldberger, A.L., Peng, C.K., “Multiscale entropy analysis of complex physiological time series,” Phys. Rev. Lett., 2002. Fava, C., Montagnana, M., Favaloro, E.J., Guidi, G.C., Lippi, G., “Obstructive Sleep Apnea Syndrome and Cardiovascular Diseases,” Semin. Thromb. Hemost., 2011; 37: p. 280-297. Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H., 2011, Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed., 2012, 108, 10–19. Fell, J., Roschke, J., Mann, K., Schaffner, C., “Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures,” Electroencephalogram Clin Neurophysiol, 1996; 98: p. 401-10. Garg, G., Singh, V., Gupta, J.R.P, Mittal, A.P., Chandra, S., “Computer Assisted Automatic Sleep Scoring System Using Relative Wavelet Energy Based Neuro Fuzzy Model,” WSEAS Transactions on Biology and Biomedicine, 2011; 8: p. 12-24. Haykin, S., “Neural networks: a comprehensive foundation,” 2nd ed. USA: Prentice Hall; 1998. Iber, C., Ancoli-Israel, S., Chesson, A., Quan, S.F., “The AASM manual for scoring of sleep and associated events: rules, terminology and technical specifications,” 1st ed. Westchester, IL: American Academy of Sleep Medicine; 2007. Kuo, C.E. and Liang, S.F., “Automatic Stage Scoring of Single-Channel Sleep EEG based on Multiscale Permutation Entropy,” IEEE Biomedical Circuit and Systems Conference (BioCAS 2011), San Diego, USA, Nov. 10-12, 2011. Liu, Y., Yan, L., Zeng, B., and Wang, W., “Automatic sleep stage scoring using Hilbert-huang transform with BP neural network,” IEEE ICBBE, 2010; pp. 1-4. Liu, Y., Yan, L., Zeng, B., Wang, W., “Automatic sleep stage scoring using Hilbert-Huang transform with BP Neural Network,” Proceedings of ICBEE; 2010: p. 1-4. Oropesa, E., Cycon, H.L., Jobert, M., “Sleep stage classification using Wavelet transform and neural network,” ISCI Technical Report TR, 1999; p. 99-008. Pandey, A., Demede, M., Zizi, F., Al Haija’a, O.A., Nwamaghinna, F., Jean-Louis, G., et al., “Sleep apnea and diabetes: insights into the emerging epidemic,” Curr Diabetes Rep, 2011; 11: p. 35-40. Pradhan, N., Narayana Dutt, D., Sadasivan, P.K., Satish, M., “Analysis of the chaotic characteristics of sleep EEG patterns from dominant Lyapunov exponents,” In: Proceedings of RC IEEE-EMBS and 14th BMESI, 1995; p. 3/79-3/80. Rechtschaffen, A., Kales, A., “A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects,” Los Angeles, CA: BIS/BRI, University of California; 1986. Schaltenbrand, N., Lengelle, R., Macher, J.P., “Neural network model: application to automatic analysis of human sleep,” Comput Biomed Res, 1993; 26: p. 157-71. Shimada, T., Tamura, K., Fukami, T., Saito, Y., “The effect of using Elman-type SOM for sleep stages diagnosis,” Proceedings of IEEE/ICME, 2010; p. 165-170. Tagluk, M.E., Sezgin, N., Akin, M., “Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG,” J Med Syst, 2010; 34: p. 717-25. Vural, C., Yildiz, M., “Determination of sleep stage separation ability of features extracted from EEG signals using principal component analysis,” J Med Syst, 2010; 34: p. 83-9. Wang, W.Y., “A sleep staging method based on single channel EOG signal,” Master thesis, 2009. Zoubek, L., Charbonnier, S., Lesecq, S., Buguet, A., Chapotot, F., “Feature selection for sleep/wake stages classification using data driven methods,” Biomed Sig Proc Control, 2007; 2: p. 171-9. 劉勝義，2004，臨床睡眠檢查學，合記圖書出版社。 錢世鍔，2005，時頻變換與小波變換導論，機械工業出版社。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60185 | - |
| dc.description.abstract | 本研究之目的在發展清醒期與睡眠階段自動辨識之方法，採用最新的AASM規則，並使用時頻分析方法和規劃睡眠階段判讀的決策樹，以符合睡眠技師人工解讀訊號的程序來辨識睡眠階段。針對以往研究常忽略的動作期偵測以及清醒期和睡眠第三期之辨識需計算特徵占整頁時間比例之規則，本研究將整頁時間切割為一秒一個片段，並逐秒計算睡眠階段辨識所需要之頻帶能量與頻帶振幅等參數，進而求出特徵占整頁時間之比例。
本研究以功率頻譜計算睡眠腦波與肌電訊號，偵測雜訊過多之動作期。而清醒期與睡眠第三期則使用時頻分析方法 WVD ( Wigner - Ville Distribution )，由WVD時頻圖之能量分布，將頻率劃分為δ波(0.5∼3.5 Hz)、θ波(4∼7.5 Hz)、α波(8∼12 Hz)、σ:波(12.5~16 Hz)和β波(16.5∼30 Hz)五條頻帶並逐秒計算頻帶能量。最後經由比較每秒各頻帶能量大小之方法來偵測頻率為α波或更高頻的訊號特徵，並計算特徵占整頁時間的比例來辨識睡眠清醒期。以及計算每秒δ頻帶的振幅參數，設定閥值來偵測高幅慢波之特徵，並計算特徵占整頁時間的比例來辨識睡眠第三期。最後依仿照人工判讀之決策樹，將整晚的睡眠階段依動作期和非動作期分類為睡眠清醒期、睡眠第三期與其他睡眠階段三個類別。 本研究的受試者為二十位健康成年人，分別為七位男性與十三位女性，年齡在20到55歲之間。將所有受試者自動分類的睡眠階段與睡眠技師判讀的睡眠階段做比較，得到睡眠清醒期的敏感度為81.08%，睡眠第三期的敏感度為80.19%，其他睡眠階段的特異度為89.90%，整體精確率達86.34%，都有相當高的準確率。此外，將本研究之結果進行信度檢驗得到之Kappa值達0.749，介於0.6~0.8之間，代表本研究的自動判讀結果與睡眠技師人工判讀結果有高度的吻合度。 | zh_TW |
| dc.description.abstract | This study develops an automatic identification of Wake and NREM-3 stages based on AASM rules. In order to follow the procedure of the artificial sleep stage scoring of sleep technicians, this study uses the time-frequency analysis and the decision tree to identify the sleep stages. Since the formerly studies often ignored the detection of MT and the rules that the identification of Wake and NREM-3 needs to count what percentage of the marks account for one epoch, this study uses an one-second window to get the feature coefficients to find what percentage of the marks account for one epoch.
At first, this study uses the EEG and EMG channel to detect the MT using the method of power spectral density. And then using the EEG signal’s time-frequency density function of Wigner - Ville Distribution to compute the band- power of five frequency bands (delta: 0.5-3.5 Hz, theta: 4-7.5Hz, alpha: 8-12Hz, sigma: 12.5-16Hz, and beta: 16.5-30Hz ) and the summation of amplitude of delta band by one-second window to find the marks of Wake and NREM-3. At last, using the decision tree form the artificial process of sleep stage scoring to classify the sleep stages into Wake, NREM-3 and the others. In this study, there are twenty healthy adults (seven males and thirteen females, age: 34.1±11.7 years). The results of auto-classification compared to those of human expert’s are sensitivity of 81.08% for Wake, sensitivity of 80.19% for N3, and specificity of 89.90% for the other sleep stages. Besides the accuracy is very high up to 86.34%. And the Kappa coefficient of agreement is 0.749 which means that this study has a substantial agreement with experts. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:13:24Z (GMT). No. of bitstreams: 1 ntu-102-R00543049-1.pdf: 3113945 bytes, checksum: b4dc94106c9d4101f24869146dd6bce1 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 致謝……………………………………………………………………………………i
中文摘要……………………………………………………………………………ii ABSTRACT…………………………………………………………………………iii 目錄…………………………………………………………………………………v 圖目錄………………………………………………………………………………vii 表目錄………………………………………………………………………………x 第一章 緒論……………………………………………………………………1 1.1 研究動機…………………………………………………………………1 1.2 文獻回顧…………………………………………………………………1 1.3 論文架構…………………………………………………………………4 第二章 腦波與睡眠階段………………………………………………………7 2.1 腦波簡介…………………………………………………………………7 2.1.1 腦波的產生…………………………………………………………7 2.1.2 腦波電極紀錄………………………………………………………8 2.1.3 腦電圖分類…………………………………………………………9 2.2 睡眠階段介紹……………………………………………………………10 2.2.1 睡眠結構…………………………………………………………10 2.2.2 睡眠階段判讀規則………………………………………………12 2.2.3 人工判讀流程……………………………………………………13 2.3 睡眠資料擷取……………………………………………………………14 2.3.1 睡眠檢查…………………………………………………………14 2.3.2 腦波量測…………………………………………………………16 第三章 ＷＶＤ時頻轉換分析………………………………………………25 3.1 時頻分析簡介……………………………………………………………25 3.2 Wigner-Ville Distribution…………………………………………………27 3.2.1 數學式推導………………………………………………………27 3.2.2 一般性質…………………………………………………………28 3.2.3 交互項干擾………………………………………………………31 3.2.4 WVD時頻圖………………………………………………………31 第四章 自動睡眠階段判讀…………………………………………………39 4.1 訊號分析流程……………………………………………………………39 4.2 訊號前處理………………………………………………………………40 4.3 特徵擷取…………………………………………………………………41 4.3.1 動作期特徵辨識…………………………………………………42 4.3.2 睡眠清醒期特徵辨識……………………………………………45 4.3.3 睡眠第三期特徵辨識……………………………………………47 4.4 睡眠階段分類……………………………………………………………48 第五章 分析結果與討論……………………………………………………69 5.1 分析結果準確率與信度方法……………………………………………69 5.2 睡眠階段辨識之精度分析………………………………………………70 5.3 總分類結果………………………………………………………………71 5.4 結果討論…………………………………………………………………72 第六章 結論與未來展望……………………………………………………87 6.1 結論………………………………………………………………………87 6.2 未來展望………………………………………………………………88 參考文獻……………………………………………………………………………91 | |
| dc.language.iso | zh-TW | |
| dc.subject | 自動判讀 | zh_TW |
| dc.subject | 時頻分析 | zh_TW |
| dc.subject | 維格納分佈 | zh_TW |
| dc.subject | 睡眠階段 | zh_TW |
| dc.subject | 睡眠腦波 | zh_TW |
| dc.subject | sleep EEG | en |
| dc.subject | sleep stage | en |
| dc.subject | automatic identification | en |
| dc.subject | time-frequency analysis | en |
| dc.subject | Wigner-Ville distribution | en |
| dc.title | 應用腦波能量分布於清醒期與睡眠第三期之辨識 | zh_TW |
| dc.title | Identification of Wake and NREM-3 Stages Using Energy Distributions of Sleep EEG | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 江秉穎(Ping-Ying Chiang) | |
| dc.contributor.oralexamcommittee | 梅興 | |
| dc.subject.keyword | 睡眠腦波,睡眠階段,自動判讀,時頻分析,維格納分佈, | zh_TW |
| dc.subject.keyword | sleep EEG,sleep stage,automatic identification,time-frequency analysis,Wigner-Ville distribution, | en |
| dc.relation.page | 93 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-20 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 應用力學研究所 | zh_TW |
| 顯示於系所單位： | 應用力學研究所 | |
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