博碩士論文 105521132 詳細資訊




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姓名 徐新閎(shin-hong Shiu)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 應用深度學習於睡眠分期判別
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摘要(中) 睡眠障礙為現代人很嚴重的文明病,如何達到完善良好的睡眠並給予適當的治
療是很重要的議題,現今很多醫生與科學家都利用大數據以及人工智慧來解決此問
題,為了達到此目的本論文使用近幾年熱門的技術深度學習(Deep learning),將數
名受測者的 PSG 中 EEG 與 EOG 進行分析,利用小波轉換(Wavelet Transform , WT)
將訊號進行時頻分析,將其中 α、β、σ、θ 四個重要特徵取出,以上得到特徵訊號
放入深度學習網路來做分析,使用的網路架構分為三個不同層的架構,第一層為多
層 感 知 層(Multilayer Perceptron ,MLP) 、第 二 層 為 長短期記 憶 (Long Short-Term
Memory,LSTM)與最後一層的歸一化指數函數(Softmax)作為睡眠週期的分類器,經
由其網路訓練準確度高達到 6 成 5,將此模型建立好之後,接下來的目標為運用在
硬體上,搭配雲端醫療系統,更能減少醫療資源,也能在自家做檢測,能減少認床
等可排除的問題,進能達到完善的治療以及資料的建檔。
摘要(英) Sleep disorder is a popular modern civil disease. It has become an important issue
of how to provide adequate treatment for sustaining well sleep. In recent years, many
doctors and scientists try to collect big data and apply artificial intelligence to solve this
problem. In this paper, we use the deep learning neural network to analyze subjects’ PSG
EEG and EOG. We used wavelet transform (WT) to decompose the measured EEG
signals into α, β, σ, and θ bands, so that the temporal-frequency parameters were
obtained as input data for deep learning neural network. The proposed network
architecture contianed three different layers. The first layer is Multilayer Perceptron
(MLP). The second layer is the Long Short-Term Memory (LSTM) and the last layer of
the normalized exponential function (Softmax) as classifiers for the sleep cycle. The
detection accuracy of the our study results was 65 percent. The continuing work of this
paper will use hardware on a cloud-based medical system to process the sleep data. With
cloud computation service, users will be able to perform self-diagnosis and homecare
service in their home.
關鍵字(中) ★ 睡眠腦波
★ 深度學習
★ 長短期記憶
★ 小波轉換
關鍵字(英)
論文目次 iv


內容
第一章 緒論 ............................................................................................................................... 1
1-1 前言 .................................................................................................................................. 1
1-2 研究動機 .......................................................................................................................... 2
1-3 文獻回顧 .......................................................................................................................... 2
1-4 論文章節架構 .................................................................................................................. 3
五、結論與未來展望。 ........................................................................................................ 3
第二章原理介紹 ........................................................................................................................ 4
2-1 大腦結構與功能 .............................................................................................................. 4
2-2 腦波分類 .......................................................................................................................... 6
2-3 睡眠週期介紹 ................................................................................................................ 10
2-3.1 睡眠分期規則 ......................................................................................................... 11
2-3.2 睡眠腦波事件評判與特徵 ..................................................................................... 12
2-4 睡眠訊號量測位置 ........................................................................................................ 17
第三章 研究方法 ..................................................................................................................... 19
3-1 受試者 (database) .......................................................................................................... 19
3-2 訊號前處理-小波轉換( Wavelet transform ) ................................................................ 19
3-2.1 小波理論分析 ............................................................................................................. 21
3-3 類神經網路 .................................................................................................................... 24
3-3.1 神經元 ..................................................................................................................... 25
3-3.2 活化函數(activation function) ................................................................................ 26
3-3.3 多層感知層(Multilayer Perceptron , 簡稱 MLP) ................................................... 27
3-3.4 遞歸神經網路(Recurrent Neural Networks) .......................................................... 28
3-3.5 長短期記憶神經網路(Long-Short Term Memory Networks LSTM) ................... 30 3-3.6 長短期記憶神經網路(Long Short-Term Memory)的向前傳遞(Forward Pass) ... 33
3-4 訊號處理與網路架構設計 ............................................................................................ 36
3-4.1 訊號處理流程 ......................................................................................................... 36
3-4.2 網路架構設計 ......................................................................................................... 37
第四章實驗結果 ...................................................................................................................... 38
第五章討論 .............................................................................................................................. 43
第六章結論與未來展望 .......................................................................................................... 45
參考文獻 .................................................................................................................................. 46
參考文獻 46


參考文獻
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[18]深度學習如熱門,https://www.zhihu.com/question/26006703
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“Mixed Neural Network Approach for Temporal Sleep Stage Classi?cation”,IEEE
指導教授 李柏磊 審核日期 2018-8-21
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