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研究生:賴柏元
研究生(外文):Bo-YuanLai
論文名稱:基於脈搏變異性分析之睡眠階層辨識演算法之研發
論文名稱(外文):Development of a Sleep Stage Recognition Algorithm Using Pulse Rate Variability Analysis
指導教授:王振興王振興引用關係
指導教授(外文):Jeen-Shing Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系
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:2015
畢業學年度:103
語文別:中文
論文頁數:76
中文關鍵詞:睡眠階層脈搏變異性辨識訊號驗證
外文關鍵詞:Sleep stagepulse rate variabilityrecognitionsignal verification
相關次數:
  • 被引用被引用:1
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  • 下載下載:18
  • 收藏至我的研究室書目清單書目收藏:0
本論文主旨在開發睡眠狀態辨識演算法,用以辨識一般人整晚的睡眠狀態。演算法ㄧ開始先將整晚的睡眠脈搏訊號做訊號前處理及雜訊消除,並將每個視窗的脈搏訊號依照時域分析、頻域分析和非線性分析三大種類計算出共32個特徵,然後將每個特徵正規化以消除生理訊號之特徵值單位不同。接著利用隨機減少多數法(random under-sampling)的方法來處理資料不平衡的問題,平衡後的資料再進行特徵降維。本論文以主成分分析(principal component analysis, PCA)及線性判別分析(linear discriminant analysis, LDA)兩種不同降維方法做比較,降維後的特徵作為分類器的輸入參數。此外,本論文使用了三種分類器來做睡眠狀態辨識的比較,第一種使用最常見且方便的最近鄰居法(k-nearest neighbors, KNN),做為比較依據。第二種分類器使用具時間及隨機特性的分類器隱藏馬可夫模型(hidden Markov model, HMM),配合具時間序列的睡眠資料來做分析。第三種分類器使用遞迴類神經網路(recurrent neural network, RNN),此分類器具有學習能力和時間特性兩大優點。本論文利用多導睡眠儀(polysomnography, PSG)收錄了34位健康受試者的資料,並將睡眠狀態分成清醒(wake)、快速動眼期(rapid eye movement, REM)和非快速動眼期(non-rapid eye movement, NREM)三個時期,最近鄰居法平均正確率(accuracy)為80.18%,隱藏馬可夫模型平均正確率為82.65%,遞迴類神經網路平均正確率為83.79%。實驗結果成功的驗證了使用脈搏訊號辨識睡眠狀態之有效性,且在未來可進一步實現在穿戴式載具上,讓睡眠監測更貼近生活且更加的便利。
This thesis proposes an algorithm for sleep stage recognition using pulse rate variability analysis. The algorithm starts with signal preprocessing, that is, signals are preprocessed in order to remove artifacts from the pulse rate signals collected by sensors. Then, with the preprocessing signal, 32 features are generated from the time-domain, frequency-domain and nonlinear analysis. The features are normalized in order to minimize the effect of differences in the ranges of values among different features. Subsequently, the random under-sampling method is utilized to remove data imbalance. This thesis applied principal component analysis (PCA) and linear discriminant analysis (LDA) to do feature dimension reduction and performance comparison. In the sleep stage recognition, three classifiers were applied and compared: a k-nearest neighbors (KNN), a hidden Markov model (HMM), and a recurrent neural network (RNN). A database with classified sleep stage wake, rapid eye movement (REM) and non-rapid eye movement (NREM) of 34 healthy subjects obtained from a hospital in the southern Taiwan was used in the experiment. The average accuracy of KNN, HMM, and RNN were 80.18%, 82.65% and 83.79%, respectively. The effectiveness of the proposed algorithm has validated by the experimental results. In the future, the proposed algorithm can be applied to wearable devices for home sleep monitoring.
中文摘要 i
英文摘要 iii
誌謝 viii
目錄 ix
表目錄 xi
圖目錄 xiii
第1章 緒論 1
1.1 研究動機與背景 1
1.2 文獻探討 2
1.2.1 睡眠狀態的定義 2
1.2.2 睡眠狀態辨識研究現況 5
1.3 研究目的 7
1.4 論文架構 8
第2章 睡眠狀態實驗架構與訊號驗證 9
2.1 實驗設置 9
2.2 心電訊號與脈搏訊號的相關性 12
2.2.1 訊號前處理 13
2.2.2 R波和P波補償 13
2.2.3 相關性驗證 18
第3章 基於脈搏訊號之睡眠狀態偵測演算法 21
3.1 特徵產生 22
3.1.1 時域分析 22
3.1.2 頻域分析 24
3.1.3 非線性分析 27
3.2 特徵正規化 30
3.3 資料平衡 30
3.4 特徵降維 31
3.4.1 主成分分析 (principal component analysis, PCA) 31
3.4.2 線性區別分析 (linear discriminant analysis, LDA) 33
3.5 辨識器 35
3.5.1 最近鄰居法 (k-nearest neighbors, KNN) 36
3.5.2 隱藏馬可夫模型 (hidden Markov model, HMM) 37
3.5.3 遞迴類神經網路 (recurrent neural network, RNN) 42
第4章 實驗結果 48
4.1 睡眠階層辨識 48
4.1.1 KNN實驗結果 49
4.1.2 HMM實驗結果 54
4.1.3 RNN實驗結果 56
4.2 睡眠效率 60
第5章 討論 65
5.1 實驗結果討論 65
5.2 與其他文獻比較 69
5.3 不同睡眠狀態種類辨識 70
第6章 結論與未來工作 72
6.1 結論 72
6.2 未來工作 73
參考文獻 74

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