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研究生:賴柏翰
研究生(外文):Po-Han Lai
論文名稱:基於眼動圖與肌電圖之自動睡眠分期系統
論文名稱(外文):Automatic Sleep Stage Recognition System Based on EOG and EMG
指導教授:段裘慶段裘慶引用關係
指導教授(外文):Chiu-Ching Tuan
口試委員:駱榮欽李財福
口試委員(外文):Rong-Chin LoTsair-Fwu Lee
口試日期:2013-07-29
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電腦與通訊研究所
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:2013
畢業學年度:101
語文別:中文
論文頁數:65
中文關鍵詞:睡眠期眼動圖肌電圖自動睡眠分期
外文關鍵詞:Sleep StageEOGEMGSleep stage recognition
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近年來由於時代的變遷及科技的進步,造成現代人生活忙碌,工作時間倍增、壓力變大,很多人會犧牲睡眠時間完成手邊的工作,睡眠品質下降,不只會導致專注力下降、影響情緒、工作效率不佳等等的結果,甚至有可能引貣交通意外。 本論文提出基於眼動圖與肌電圖之自動睡眠分期系統,希望在進行睡眠檢查時,減少所需量測項目,僅使用眼動圖和肌電圖的生理訊號進行睡眠判讀,達到降低成本、減少電極片與訊號線的目的。實驗資料取自於Sleep-EDF(Sleep Recordings and Hypnograms in European Data Format)資料庫,8位受測者之眼動圖與肌電圖經過訊號前置處理與特徵擷取之後,建立一決策樹,以二元分類法之方式,透過三個分類器根據各個睡眠期之生理特徵分別設定不同門檻值,依序將快速動眼期、慢波睡眠期、清醒期與淺眠期辨識出來。
在實驗結果中,單獨以分類器效能而言,快速動眼期分類器的kappa值為三者中最佳,慢波睡眠期為次佳,再來才是清醒期,kappa值分別為82.46%,77.77%,75.35%,平均為78.5%,但若透將分類器進行不同排序,將三分類器中kappa值最
高之快速動眼期分類器進行第一階段篩選,篩選後的資料再經由次佳與次次佳之慢波睡眠器與清醒期分類分類器進行分析,可將kappa值提升到81.7%。

Sleep is very important for a person’s physical and mental health. We spend one third of our life. Most people don’t attach much importance to sleep, even ignored sleep problem. In this paper, we describe attempts at constructing an automatic sleep stage recognition system based on EOG (Electrooculogram) and EMG (Electromyography). Wake stage (WS), light sleep (LS), slow wave sleep (SWS) and rapid eye movement (REM) were classified by decision tree with three classifiers. The EOG horizontal and EMG submental data used for this analysis was obtained from the Sleep-EDF (Sleep Recordings and Hypnograms in European Data Format) database.
The concordance between Sleep-EDF database and recognition was evaluated by Cohen’s Kappa index. In results, the kappa coefficient of REM classifier, SWS classifier and WS classifier is 82.46%, 77.77% and 75.35%. Besides, we arranged three classifiers in different orders. We raised up the average of kappa coefficient to 81.7% from 78.53% when we arranged REM classifier to be a first order classifer, SWS classifier and WS classifier is second and third order.


中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 2
1.3 論文架構 3
第二章 文獻探討 5
2.1 睡眠狀態 5
2.1.1 正常睡眠 5
2.1.2 睡眠障礙 6
2.1.3 睡眠週期 6
2.1.4 睡眠期判讀 8
2.1.5睡眠期的生理狀態及特徵 8
2.1.5.1 快速動眼期 (Rapid Eye Movement, REM) 9
2.1.5.2 非快速動眼期 (Non Rapid Eye Movement, NREM) 10
2.1.5.3 清醒期 10
2.1.5.4 慢速眼球移動 10
2.1.6 睡眠參數的記錄 11
2.1.6.1 眼動圖 11
2.1.6.2 肌電圖 12
2.2 睡眠檢查 14
2.2.1 多項生理睡眠檢查儀 ( Polysomnography, PSG ) 14
2.2.2 腕動計監測 15
2.3 睡眠資料庫 16
2.3.1 PhysioBank 16
2.3.2 Sleep-EDF資料庫 16
2.4 自動睡眠分期系統 16
2.4.1 使用EEG, EOG, EMG分期 17
2.4.2 簡化量測項目 17
第三章 基於EOG與EMG之自動睡眠分期系統 21
3.1 前置處理 22
3.2 自動睡眠分期 23
3.2.1 決策樹之架構 23
3.2.2 快速動眼期分類器 24
3.2.3 慢波睡眠分類器 28
3.2.4 清醒期分類器 30
第四章 實驗結果與分析 33
4.1 量測參數 33
4.2 效能評估因子 34
4.2.1 敏感度與特異度 35
4.2.2 Cohen’s kappa 36
4.3 實驗結果分析與比較 38
4.3.1辨識睡眠期之敏感度、特異度、Cohen’s kappa分析 38
4.3.1.1 快速動眼期分類器 39
4.3.1.2 慢波睡眠期分類器 42
4.3.1.3 清醒期分類器 46
4.3.2 分類器排序分析 50
第五章 結論與未來研究方向 53
5.1 結論 53
5.2 未來研究方向 54
參考文獻 55
附錄A:中英文專有名詞對照表 59
附錄B:作者簡歷 64


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