請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55948
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 闕志達(Tzi-Dar Chiueh) | |
dc.contributor.author | Yi-Hao Huang | en |
dc.contributor.author | 黃怡豪 | zh_TW |
dc.date.accessioned | 2021-06-16T05:11:26Z | - |
dc.date.available | 2016-08-25 | |
dc.date.copyright | 2014-08-25 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-18 | |
dc.identifier.citation | [1] A. Rechtschaffen, and A Kales, A manual of standardized terminology, techniques and scoring system for sleep stages of human subject. Washington DC: US Government Printing Office, National Institute of Health Publication, 1968.
[2] Rest Analysis, USA [Online]. Available: http://restanalysis.com/services.php?page=sleep_study [3] N. A. Collop, W. M. Anderson, B. Boehlecke, et al.,” Clinical Guidelines for the Use of Unattended Portable Monitors in the Diagnosis of Obstructive Sleep Apnea in Adult Patients”, J Clin Sleep Med, Vol. 3, No. 7, pp. 737-747, 2007. [4] WIKIPEDIA Sleep apnea. Available: http://en.wikipedia.org/wiki/Sleep_apnea [5] R. Ruehland, D. Rochford, J. O'Donoghue, et al., 'The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index,' Sleep, Vol.32(2), pp.150–157,Feb 2009. [6] Surgical Sleep Solutions, “Obstructive Sleep Apnea,” [Online]. Available: http://surgicalsleepsolutions.com/obstructive-sleep-apnea/ [7] M. Xiao, H. Yan, J. Song, Y. Yang, and X. Yang, “Sleep stages classification based on heart rate variability and random forest,” Elsevier Biomedical Signal Processing and Control, Vol.8, pp.624–633, November 2013. [8] B. Yılmaz, M. H. Asyalı, E. Arıkan, S. Yetkin, and F. Ozgen, “Sleep stage and obstructive apneaic epoch classification using single-lead ECG”, BioMedical Engineering OnLine, Vol.9, 9:39, August 2010. [9] Y. Li, F. Yingle, and L. Gu, “Sleep Stage Classification based on EEG Hilbert-Huang Transform,” in Proc. of IEEE Conf. on Industrial Electronics and Applications, Xi'an, May 2009, pp. 3676-3681. [10] K. ˇSuˇsmakova, and A. Krakovska, “Discrimination ability of individual measures used in sleep stages classification,” Elsevier Artificial Intelligence in Medicine, Vol.44, pp.261–277, November 2008. [11] S. Gunes , K. Polat, and S. Yosunkaya, “Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting,” Elsevier Expert Systems with Applications, Vol.37, pp.7922–7928, December 2010. [12] PhysioNET, [Online]. Available: http://www.physionet.org/pn3/ucddb/ [Last time visited: 1/10/2009] [13] J. N. Knight, ”Signal Fraction Analysis and Artifact Removal in EEG,” THESIS for the Degree of Master of Science Colorado State University. [14] C. Levkov, G. Mihov, R. Ivanov, et al., “Removal of power-line interference from the ECG: a review of subtraction procedure,” BioMedical Engineering OnLine, Vol.4, 4:50, August 2005. [15] B. Mozaffary, and M. A. Tinati, “ECG Baseline Wander Elimination using Wavelet Packets,” World Academy of Science Engineering and Technology, vol. 3, pp. 14–16, August 2005. [16] S.-H. Liu, “Motion artifact reduction in electrocardiogram using adaptive filter,” Medical and Biological Engineering, vol. 31, pp. 64–72, November 2011. [17] M. Kaur, B. Singh, and Seema, “Comparisons of Different Approaches for Removal of baseline wander from ECG signal,” in Proc. of IEEE Conf. on International Conference and Workshop on Emerging Trend (ICWET), Mumbai, Maharashtra, India, February 2011, pp. 30-34. [18] K.-M. Chang, “Arrhythmia ECG noise reduction by ensemble empirical mode decomposition,” Sensors, vol. 10, pp. 6063–6080, April 2010. [19] N. E. Huang, Z. Shen, S. R. Long, et al., “The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis,” in Proc. of Roy. Soc. Conf. on Proceedings A, London, March 1998, pp. 903–995. [20] N. E. Huang, and S. S. P. Shen, Hilbert–Huang Transform and Its Applications. Singapore: World Scientific, Interdisciplinary Mathematical Sciences, 2005. [21] WIKIPEDIA Hilbert-Huang transform. Available: http://zh.wikipedia.org/wiki/ Hilbert-Huang transform [22] Z. Wu, and N. E. Huang, 'Ensemble empirical mode decomposition: A noise-assisted data analysis method,' Advanced Adaptive Data Analysis, vol. 1, pp. 1–41, January 2009. [23] A. Delorme,T. Sejnowski, and S. Makeig, “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis,” Elseviser NeuroImage, vol. 4, pp. 15–34, December 2006. [24] WIKIPEDIA Excess kurtosis. Available: http://www.bogleheads.org/wiki/Excess_kurtosis [25] I. Conrad, A.-I. Sonia, L. Andrew, et al., “The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications,” Westchester: American Academy of Sleep Medicine, 2007. [26] Akes on a plane, “Pwasa Problems,” [Online]. Available: http://akesonaplane.blogspot.tw/2012/08/pwasa-problems.html [27] HEAL YOURSELF AT HOME,” Different Stages of Sleep,” [Online]. Available: http://healyourselfathome.com/ [28] A. D. Krystal, J. D. Edinger, W. K. Wohlgemuth, and G. R. Marsh,” NREM Sleep EEG Frequency Spectral Correlates of Sleep Complaints in Primary Insomnia Subtypes,” SLEEP, Vol. 25, No. 6, September 2002. [29] Sleep Stages - EEG scoring. [Online]. Available: http://www.sleepstudy.org/ [30] WIKIPEDIA Sleep spindle. Available: http://zh.wikipedia.org/wiki/ Sleep spindle [31] T.-B. Kuo , C.-Y. Chen, Y.-C. Hsu, “Performance of the frequency domain indices with respect to sleep staging,” Elsevier Clinical Neurophysiology, Vol. 123, No. 7, July 2012. [32] R. Armitage, G. Emslie, and J. Rintelmann, “The effect of fluoxetine on sleep EEG in childhood depression: a preliminary report,” Neuropsychopharmacology, Vol. 17, No. 4, October 1997. [33] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review, Vol. E, No. 71, February 2005. [34] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale Entropy Analysis of Complex Physiologic Time Series,” Physical Review Lett., Vol. 89, No. 6, August 2002. [35] J. C. David, Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003. [36] WIKIPEDIA K-means clustering. Available: http://en.wikipedia.org/wiki/K-means_clustering [37] WIKIPEDIA Markov chain. Available: http://en.wikipedia.org/wiki/Markov_chain [38] S. P. Meyn, and R.L. Tweedie, Markov Chains and Stochastic Stability, London, Springer-Verlag, 1993. [39] WIKIPEDIA Artificial neural network. Available: http://en.wikipedia.org/wiki/Artificial_neural_network [40] R. Salakhutdinov, G. Hinton, “An Efficient Learning Procedure for Deep Boltzmann Machines,” Neural Computation, Vol. 24, pp. 1967-2006, August 2012. [41] Geoffrey Hinton, “A Practical Guide to Training Restricted Boltzmann Machines,” Lecture Notes in Computer Science, Vol. 7700, pp. 599-619, August 2010. [42] Y. Yuan and M.J. Shaw, “Induction of fuzzy decision trees,” Fuzzy Sets and Systems 69 (1995), pp. 125–139. [43] R. B. Berry, R. Budhiraja , D. J. Gottlieb, et al., ”Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine,” J Clin Sleep Med, Vol. 5, No. 8, October 2012. [44] T.-Y. Huang, “Comparing the influences of CSA and OSA on ECG signals,” THESIS for the Degree of Master of Mechanical & Electro-Mechanical Engineering National Sun Yat-sen University. [45] EasyOxygen, Nasal cannula. [Online]. Available: http://www.easyoxygen.com.au/ [46] BRAEBON, Oral only Thermistor. [Online]. Available: http://www.medcat.nl/supplies/En/braebon.htm [47] WIKIPEDIA Pulse oximetry. Available: http://en.wikipedia.org/wiki/ Pulse_oximetry [48] M. K. Erman, D. Stewart, D. Einhorn, et al., “Validation of the ApneaLink™ for the screening of sleep apnea: a novel and simple single-channel recording device,” J Clin Sleep Med, Vol. 4, No. 3, June 2007. [49] S. S. NG, T. O. Chan, K. W. To, et al., “Validation of Embletta portable diagnostic system for identifying patients with suspected obstructive sleep apnea syndrome (OSAS.),” Respirology, Vol. 2, No. 15, pp. 336–342, February 2010. [50] H. S. Driver, E. J. Pereira, K. Bjerring, et al., “Validation of the MediByteR type 3 portable monitor compared with polysomnography for screening of obstructive sleep apnea,” Can Respir J, Vol. 3, No. 18, May 2011. [51] N. T. Ayas, S. Pittman, M. MacDonald, and D. P. White, “Assessment of a wrist-worn device in the detection of obstructive sleep apnea,” Sleep Med, Vol. 5, No. 4, September 2003. [52] J. A. Swets, Signal detection theory and ROC analysis in psychology and diagnostics. Mahwah, NJ, Lawrence Erlbaum Associates, 1996. [53] A. A. Poli, and M. C. Cirillo, “On the use of the normalized mean square error in evaluating dispersion model performance,” ELSEVIER Atmospheric Environment, Vol. 27, pp. 2427-2434, October 1993. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55948 | - |
dc.description.abstract | 對睡眠障礙患者而言,Polysomnography(PSG)是目前最好的方式來檢查患者的睡眠階段,並藉此了解患者的睡眠品質(Sleep quality),診斷各式睡眠疾病,例如阻塞型睡眠呼吸中止症(Obstructive sleep apnea)。然而判別睡眠階段需檢測至少八種不同的生理特徵,包含:腦電訊號、眼動訊號和肌電訊號而且人工判讀亦相當花費時間以及高成本,因此許多研究嘗試減少測量的參數以及設計適合自動判讀的演算法。雖然目前已經有很多研究提出了不同的自動判讀睡眠階段方法運用少量的參數做預測。但平均準確率大多都不及80%,且無法兼顧每種睡眠層級。直到現在尚未有一種簡易型的睡眠預測,可以在醫學上被各界認定有效且被廣泛使用。
本論文提出一個舒適且簡易的方式進行睡眠階段的自動判讀。可以讓使用者在家裡就可以接受檢測,就可以達到類似於PSG的成果。此項全新的演算法僅利用2-lead的腦電訊號和1-lead的肌電訊號加上透過神經網路為基礎的決策樹(Neural-Network-based decision tree),搭配馬可夫鏈的概念,即可將睡眠正確分期,最好的準確率為82.6%。補提藉此減少成本或工作時間。 此外本論文亦提出睡眠呼吸中止症自動判讀的演算法。針對OSA患者進行檢測。透過可攜式檢測器(Portable Monitor)對使用者進行呼吸中止自動判讀,並換算成AHI指數(Apnea–Hypopnea Index),量化OSA的嚴重程度。搭配上述的睡眠階段自動判讀系統,可以建立起完整的睡眠檢查制度,讓睡眠醫療更為方便、普及。 | zh_TW |
dc.description.abstract | For patients with sleep disorder, Polysomnography (PSG) is the best method to analyze their sleep stage and understand sleep quality. By taking a whole-night examination, the sleep laboratory can diagnose several kinds of sleep diseases such as obstructive sleep apnea. However, PSG requires at least eight different physiological signals, including EEG C3, C4, EOG and EMG, to analyze the sleep stage. Besides, manual interpretation is also costly and time-consuming. Therefore, many researches try to reduce the number of channels required to classify sleep stage automatically. Although there are several works announced that used different methods, the average accuracy is still low, mostly under 80%, and hard to give consideration to each stage’s accuracy. So far there is no portable monitor that is recommended and widely used in medical field.
An automated sleep stage recognition system is proposed in this thesis. This system allow users to take the examination in their own homes. The function is very similar to PSG analysis. This novel algorithm only uses 2-lead EEG and 1-lead EMG signals. By using a neural-network-based decision tree and the Markov Model technique, our system can recognize sleep stage effectively. The accuracy is 82.6%. Furthermore, an OSA detection algorithm is also proposed in this thesis. The target is the patients who have sleep apnea. The specific Portable monitor can detect the events when sleep apnea happens and calculate the Apnea–Hypopnea Index (AHI). It can point out the severity of OSA. Combining automated sleep stage recognition and OSA detection system, we expect to develop a better sleep analysis mechanism. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T05:11:26Z (GMT). No. of bitstreams: 1 ntu-103-R01943036-1.pdf: 4098383 bytes, checksum: d9994727698f8d82d6b88359dba8e4d4 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書………..…………………………………………….……………..I
誌謝…………………...…..……………………………………………..………...…III 摘要……………………...……………………………...…………………..….………V ABSTRACT…………..……………………………………………….…….………VII 圖目錄 XII 表目錄 XIV 1. 第一章 緒論 1 1.1 研究動機 1 1.2 呼吸中止症簡述 3 1.3 本論文提出解決之方法 4 1.4 論文組織 5 2. 第二章 睡眠階段自動判讀系統 7 2.1 過往自動判讀的研究結果 7 2.2 系統架構 9 2.3 資料收集 9 2.4 信號前處理 10 2.5 特徵值擷取 11 2.6 階段判讀 11 3. 第三章 信號前處理 12 3.1 腦電圖雜訊簡介 12 3.2 臨床資料 13 3.2.1 Cohort-A 13 3.2.2 Cohort-B 14 3.2.3 Cohort-C 14 3.3 集成經驗模態分解法去除雜訊 14 3.3.1 經驗模態分解法 15 3.3.2 集成經驗模態分解法 20 3.4 雜訊片段去除 23 3.4.1 極端值 23 3.4.2 頻帶 24 3.4.3 線性趨勢 24 3.4.4 機率分布函數 25 3.4.5 峰度 26 4. 第四章 特徵值擷取 28 4.1 睡眠階段介紹 28 4.2 各睡眠階段特性 30 4.2.1 清醒期 31 4.2.2 非快速眼動期 Stage1 31 4.2.3 非快速眼動期 Stage2 32 4.2.4 非快速眼動期 Stage3 32 4.2.5 快速眼動期 33 4.3 特徵值介紹 33 4.3.1 腦電訊號頻帶 33 4.3.2 腦電訊號波形 36 4.3.3 肌電訊號強度 36 4.3.4 眼動訊號相關係數 37 4.4 多尺度熵分析 37 4.4.1 多尺度熵介紹 38 4.4.2 多尺度熵對於睡眠階段的特性 39 4.4.3 量化多尺度熵的方法 40 4.4 特徵值選取 41 5. 第五章 階段判讀 43 5.1 本論文提出的階段判讀方法 43 5.1.1 類神經網路為基礎的決策樹 43 5.1.2 K-Means分群演算法 44 5.1.3 馬可夫模型 45 5.2 K-MEANS分群演算法 46 5.2.1 K-Means的概念 46 5.2.2 K-Means演算法流程 47 5.2.3 K-Means運用於本論文 48 5.3 類神經網路 49 5.3.1 倒傳遞類神經網路 49 5.3.2 深層學習類神經網路 53 5.3.2.1 架構及演算法 54 5.3.2.2 延伸型限制性波茲曼機演算法 54 5.4 決策樹 57 5.4.1 決策樹的概念 57 5.4.2 決策樹的優點 58 5.4.3 決策樹的缺點 58 5.4.4 改進決策樹的演算法 58 6. 第六章 睡眠階段自動判讀系統之表現與臨床實驗結果 60 6.1 睡眠階段自動判讀系統之實驗結果 60 6.2 不同AHI下本系統的表現 64 6.3 Cohort B與Cohort C交叉驗證 65 6.4 綜合判定 68 6.5 本論文結果與過往研究比較 70 7. 第七章 呼吸中止自動判讀系統 71 7.1 呼吸中止與淺呼吸 71 7.2 檢測呼吸中止之儀器 72 7.3 市售Portable Monitor產品表現 73 7.4 呼吸中止自動判讀之系統架構 75 7.4.1 找出區域參考值 75 7.4.2 抓出可能的事件 76 7.4.3 重新檢察可能的事件 78 7.5 更改臨界值 78 7.5.1 簡介受試者工作特徵曲線 78 7.5.2 本系統之受試者工作特徵曲線 79 8. 第八章 呼吸中止自動判讀系統之表現與臨床實驗結果 81 8.1 睡眠呼吸中止症自動偵測之實驗結果 81 8.2 估計值與理論值的誤差分析 84 8.3 現有產品下本系統的定位 86 9. 第九章 結論與展望 87 參考文獻 89 | |
dc.language.iso | zh-TW | |
dc.title | 睡眠階段及睡眠呼吸中止症自動判讀系統 | zh_TW |
dc.title | Automated Sleep Stage Recognition and OSA Detection System | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 李佩玲(Pei-Lin Lee) | |
dc.contributor.oralexamcommittee | 吳安宇(An-Yeu Wu),曹恆偉(Hen-Wai Tsao) | |
dc.subject.keyword | 睡眠階段分期,決策樹,類神經網路,馬可夫鏈, | zh_TW |
dc.subject.keyword | sleep stage recognition,decision tree,Neural Network,Markov Model, | en |
dc.relation.page | 94 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-103-1.pdf 目前未授權公開取用 | 4 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。