| 姓名 |
吳佳俊(Jia-Jyun Wu)
查詢紙本館藏 |
畢業系所 |
電機工程學系 |
| 論文名稱 |
應用深度學習之睡眠分期辨識
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| 相關論文 | |
| 檔案 |
 [Endnote RIS 格式]
[Bibtex 格式]
 [相關文章]  [文章引用]  [完整記錄]  [館藏目錄]  至系統瀏覽論文 ( 永不開放)
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| 摘要(中) |
患有睡眠障礙的患者可能影響他們的整體健康，安全和生活品質。睡眠不佳與高血壓、動脈粥樣硬化（膽固醇阻塞的動脈）、心力衰竭、心臟病發作、中風、糖尿病和肥胖有關。然而，監測睡眠狀況需要醫生檢查大量的腦電圖數據，需要花費大量的時間，可能會有主觀判斷的問題。因此，開發用於自動且準確地檢測EEG數據中睡眠狀況的客觀平台是很重要的。在本文中，我們開發了一種基於深度學習的腦電數據睡眠分析系統。我們採用深度學習網路進行睡眠EEG分析，受測者的EEG進行小波轉換將訊號的頻帶特徵取出，再將其八個特徵值(最大值、最小值、中位數、平均值、標準差、全距、偏度、峰度)放入深度學習網路來做分析，其網路架構為LSTM，經由其網路所得到得準確度高達到83%，將取出八個特徵值以及直接時頻分析的結果做比較，發現取出八個特徵的效果比較好。接下來的目標為運用在硬體上，搭配雲端醫療系統，更能降低醫療資源，進而達到完善的治療以及資料的建檔。 |
| 摘要(英) |
Patients who suffer from sleep disorder can affect their overall health, safety and quality of life. Poor sleep has been linked with high blood pressure, atherosclerosis (cholesterol- clogged arteries), heart failure, heart attack,stroke , diabetes, and obesity. However, monitoring of sleep conditions requires physicians to examine huge amount of EEG data which is time-consuming and might fall into the pitfall of subjective judgement. Accordingly, development of an objective platform for automatic and accurate detection of sleep condition in EEG data is important. In this paper, we developed a deep learning based sleep evaluation system for EEG data. We adopted deep learning techniques to analyze sleep EEG data. The EEG data recorded from Fp1, Fp2,Pz and Oz positions were firstly transformed into temporal-frequency domain using wavelet analysis as training features. Eight characteristic features (maximum, minimum, median, average, standard deviation, range, skewness, kurtosis) on temporal-frequency domain were used as input data to train a LSTM network. The accuracy of the network is 83%. Comparing the eight eigenvalues and the results of the direct time-frequency analysis, we found that the performance of extracting eight features is better. The next goal is to use on hardware and the cloud medical system. The development of this sleep EEG analysis technique can reduce medical resources and facilitate the documentation of subject’s sleep history for better treatment in the future. |
| 關鍵字(中) |
★ 睡眠腦波
★ 深度學習
★ 小波轉換
★ 長短期記憶
★ 雲端
★ 穿戴式 |
關鍵字(英) |
★ brain wave
★ deep learning
★ wavelet transform
★ Long Short-Term Memory(LSTM)
★ cloud
★ wearable |
| 論文目次 |
目錄
中文摘要 i
Abstract ii
致謝 iii
圖目錄 vi
表目錄 viii
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 文獻回顧 3
1-4 論文章節架構 4
第二章 原理介紹 5
2-1 睡眠週期簡介 5
2-2 腦波分類 6
2-3 睡眠分期與腦波的關係 9
2-4 睡眠多項生理檢查(Polysomnography-PSG) 14
2-5 睡眠腦波量測位置 15
第三章 研究方法 16
3-1 受試者(Database) 16
3-2 訊號前處理-小波轉換( Wavelet transform ) 16
3-3 特徵擷取 20
3-4 類神經網路 22
3-4-1 神經元與活化函數 23
3-4-2 優化器(optimizer) 27
3-5 實驗設計 29
第四章 實驗結果與討論 31
4-1 長短期記憶網路於統計特徵以及輸入通道的討論 31
4-2 k fold cross validation 33
4-3 與其他網路架構的比較(CNN、SVM) 35
4-3-1 卷積神經網路(Convolutional Neural Network 簡稱CNN) 35
4-3-2 支援向量機(support vector machine 簡稱SVM) 37
4-4 直接以時頻分析 38
4-4-1 LSTM 38
4-4-2 CNN 40
4-5 輸入的排序 41
第五章 結論與未來展望 44
第六章 參考文獻 45
附錄 Database資料 48 |
| 參考文獻 |
〔1〕 台灣睡眠醫學學會第 14 屆學術研討會，取自http://www.tssm.org.tw/file/1483682380.pdf
〔2〕 Proposed supplements and amendments to ‘A Manual of Standardized Terminology,Techniques and Scoring System for Sleep Stages of Human Subjects’, the Rechtschaffen & Kales (1968) standard
〔3〕 C.-S. Huang, C.-L. Lin, L.-W. Ko, S.-Y. Liu, T.-P. Sua, and C.-T. Lin,“A hierarchical classification system for sleep stage scoring via forehead eeg signals”,in Computational Intelligence, Cognitive Algorithms, Mind,and Brain (CCMB), 2013 IEEE Symp. IEEE, 2013, pp. 1–5.
〔4〕 Akara Supratak, Hao Dong, Chao Wu, and Yike Guo, “ DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG”, IEEE Transactions on Neural Systems and Rehabilitation Engineering ( Volume: 25 , Issue: 11 , Nov. 2017 )
〔5〕 Sudhansu Chokroverty，Atlas of sleep medicine ，2e，pp.77
〔6〕 大紀元，取自http://www.epochtimes.com/b5/14/10/10/n4268496.htm
〔7〕 元智大學最佳化設計實驗室，取自http://designer.mech.yzu.edu.tw/articlesystem/article/compressedfile/(2005-04-03)%20P-Series%20PS2%E7%9D%A1%E7%9C%A0%E5%A4%9A%E9%A0%85%E7%94%9F%E7%90%86%E6%AA%A2%E6%9F%A5%E5%84%80%E4%BB%8B%E7%B4%B9.aspx?ArchID=747
〔8〕 https://zh.wikipedia.org/wiki/%E8%85%A6%E6%B3%A2
〔9〕 國立台灣大學睡眠實驗室，取自https://sleep-laboratory-at-ntu.webnode.tw/%E5%B8%B8%E8%A6%8B%E5%95%8F%E9%A1%8C/%E4%BD%95%E8%AC%82%E7%9D%A1%E7%9C%A0%EF%BC%9F/
〔10〕 https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A0%85%E7%94%9F%E7%90%86%E7%9D%A1%E7%9C%A0%E6%AA%A2%E6%9F%A5
〔11〕 台大醫院睡眠中心，取自https://www.ntuh.gov.tw/SLP/ser/DocLib1/%E6%95%B4%E5%A4%9C%E7%9D%A1%E7%9C%A0%E5%A4%9A%E9%A0%85%E7%94%9F%E7%90%86%E6%AA%A2%E6%9F%A5.aspx
〔12〕 brainproducts，取自https://www.brainproducts.com/productdetails.php?id=15
〔13〕 S. L. Himanen and J. Hasan, "Limitations of Rechtschaffen and Kales," Sleep Medicine Reviews, vol. 4, pp. 149-167, 2000.
〔14〕 http://www.wikiwand.com/zh-tw/%E7%9F%AD%E6%99%82%E8%B7%9D%E5%82%85%E7%AB%8B%E8%91%89%E8%AE%8A%E6%8F%9B
〔15〕 http://www.wikiwand.com/zh-tw/%E5%B0%8F%E6%B3%A2%E5%88%86%E6%9E%90
〔16〕 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews and Yike Guo, "Mixed Neural Network Approach for Temporal
Sleep Stage Classification",IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
〔17〕 https://zh.wikipedia.org/wiki/%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C
〔18〕 張斐章、張麗秋，2015，類神經網路導論原理與應用，滄海圖書
〔19〕 http://cvfiasd.pixnet.net/blog/post/275774124-%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E6%BF%80%E5%8B%B5%E5%87%BD%E6%95%B8%E4%BB%8B%E7%B4%B9
〔20〕 http://arbu00.blogspot.com/2017/05/3-rnn-recurrent-neural-networks.html
〔21〕 https://ithelp.ithome.com.tw/articles/10193678
〔22〕 https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF-%E5%A4%9A%E5%B1%A4%E6%84%9F%E7%9F%A5%E6%A9%9F-multilayer-perceptron-mlp-%E5%90%AB%E8%A9%B3%E7%B4%B0%E6%8E%A8%E5%B0%8E-ee4f3d5d1b41
〔23〕 https://www.cnblogs.com/guoyaohua/p/8542554.html
〔24〕 https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG)
〔25〕 Orestis Tsinalis, Paul M. Matthews, Yike Guo, and Stefanos Zafeiriou ,”Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks,” arXiv preprint arXiv:1610.01683, 2016.
〔26〕 https://www.physionet.org/content/sleep-edfx/1.0.0/
〔27〕 https://medium.com/@daniel820710/%E5%88%A9%E7%94%A8keras%E5%BB%BA%E6%A7%8Blstm%E6%A8%A1%E5%9E%8B-%E4%BB%A5stock-prediction-%E7%82%BA%E4%BE%8B-1-67456e0a0b
〔28〕 SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach |
| 指導教授 |
李柏磊(Po-Lei Lee)
|
審核日期 |
2019-10-15 |
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