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研究生:吳佳河
研究生(外文):WU, CHIA-HO
論文名稱:經驗模態分解法應用在ECG信號雜訊分離及睡眠辨識
論文名稱(外文):Application of empirical mode decomposition on noise cancellation of ECG signals and sleep stages recognition
指導教授:潘欣泰
指導教授(外文):PAN, SHING-TAI
口試委員:歐陽振森吳志宏賴智錦
口試日期:2017-07-24
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:資訊工程學系碩士班
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:2017
畢業學年度:105
語文別:中文
論文頁數:83
中文關鍵詞:經驗模態分解法隱藏式馬可夫模型睡眠狀態判讀
外文關鍵詞:Empirical Mode Decomposition (EMD)ECG, sleep stagingDiscrete Hidden Markov Model (DHMM)
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本研究採用對睡眠干擾較低且量測設備便宜的心電圖信號(ECG)來開發睡眠狀態自動判讀系統。由於心電圖信號測量的過程中會有許多雜訊的干擾,為了能夠正確判讀,本研究使用經驗模態分解法(Empirical Mode Decomposition, EMD)來提升具有環境雜訊下之心電圖信號的辨識率及抗雜訊能力,使環境雜訊對判讀結果的影響降到最低。在消除雜訊後,接著計算心電圖信號的最佳化睡眠特徵值。接下來,再利用隱藏式馬爾可夫模型(Hidden Markov Model, HMM)進行睡眠判讀,由於該模型可以對一段連續時間序列的狀態轉移機率及觀察狀態機率來分析,因此HMM適合具有連續階段轉移特性的睡眠階段辨識。
The goal of this study is to develop an automatic sleep stages recognition system based on the ECG signals. This is because that the device for measuring ECG signals is cheap and is portable such that the sleep of users will not be disturbed. Since the ECG signals are easily interfered with noises, the Empirical Mode Decomposition(EMD) method is applied in this study for cancelling the noises. Moreover some optimal ECG features for sleep staging are calculated. Then, the ECG features are used to train Hidden Markov Model(HMM), recognize the sleep stages by using the trained HMM.
The experimental results reveal that the proposed method performs well in noises cancellation.
目錄
第一章 緒論 1
1.1 前言 1
1.2 研究動機 3
1.3 論文架構 4
第二章 研究背景與原理 5
2.1 心臟構造及功能介紹 5
2.2 心電圖介紹 7
2.3 心律變異度分析 15
2.5 睡眠階段分類和定義 23
第三章 研究方法 26
3.1 心電訊號結合經驗模態分解法(EMD) 26
3.1.1 本質模態函數(IMF) 26
3.1.2 經驗模態分解法(EMD) 27
3.2 隱藏式馬爾可夫模型(HMM) 30
3.2.1 向量量化 30
3.2.2 離散隱藏式馬爾可夫模型 31
3.2.3 離散隱藏式馬爾可夫模型的訓練 32
3.2.4 離散隱藏式馬爾可夫模型的狀態猜測 36
第四章 實驗流程與結果 38
4.1 實驗方法 38
4.2 實驗結果 44
第五章 結論與未來展望 67
5.1 結論. 67
5.2 未來展望 68
參考文獻. 69

參考文獻
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