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系統識別號 U0026-0709201804522100
論文名稱(中文) 新型眼罩之睡眠分期法則開發與驗證
論文名稱(英文) Development and Validation of Sleep Staging Rules for a New Designed Eyemask
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 106
學期 2
出版年 107
研究生(中文) 王俊棋
研究生(英文) Jyun-Ci Wang
學號 P76054282
學位類別 碩士
語文別 英文
論文頁數 37頁
口試委員 指導教授-梁勝富
口試委員-王淵弘
口試委員-蕭富仁
口試委員-郭至恩
中文關鍵字 睡眠判讀  眼動電訊號  腦電訊號  睡眠品質 
英文關鍵字 Sleep staging  electrooculogram  EOG  electroencephalogram  sleep quality 
學科別分類
中文摘要 睡眠是影響生活品質不可或缺的一環,人類一生會花上三分之一的時間在睡眠上,而現今多數研究更認為睡眠更是在能量的儲存以及學習記憶中扮演了極為重要的角色,但並不是所有人都能擁有良好的睡眠品質,因此在臨床上多半會使用多通道睡眠紀錄儀器 (PSG) 紀錄並藉此觀察患者的睡眠品質以助症狀的診斷。然而睡眠紀錄儀器必需使用較多電極,不僅操作困難,對患者在睡眠品質上亦有較大的干擾,使得判讀結果無法真正反映病患平日的睡眠情況,因此在本研究中我們利用較不干擾睡眠的眼動電訊號 (EOG) 開發一套自動睡眠判讀演算法。
過去的自動睡眠判讀演算法多將眼動電訊號視為快速眼動睡眠期的輔助判讀工具,並未將眼動電訊號視為重要的睡眠生理指標之一。本研究中我們發現眼動電訊號頻道除了可以用來擷取眼動特徵外,也能夠擷取到部分睡眠腦電訊號的特徵,因此眼動訊號可以同時保有兩者的訊號特性之優點。在本研究中我們整理了一系列眼動電訊號在睡眠中會出現的眼動以及腦電訊號特徵及規則,最終我們並結合此兩者,開發出一個基於眼動電訊號的判讀方法,在經過16未受測者的訓練以及測試後,與多通道睡眠紀錄儀器判讀結果對比,可以達到90.82%的整體一致性,證明眼動訊號在睡眠階段分類上的可用性,未來或許能取代一般腦電圖紀錄並應用於臨床或簡易居家健康照護系統等相關領域上。
英文摘要 Sleep is very important for human beings. People spend one third of life on sleep. Many studies today indicate that sleep play a significance role on energy storage and learning. However there’s not everyone who can acquire good sleep quality. Thus on the clinical diagnosis, doctors use polysomnographic (PSG) recording to analyze and evaluate sleep quality of patients and diagnosis specific sleep diseases. Nevertheless, the large amount of wires for conventional PSG often makes disturbance to user. Thus the recording of sleep cannot reflect the user sleep condition as usual. Our goal is to design an automatic sleep staging method based on EOG which disturbs the user sleep much less than PSG recordings.
Most automatic sleep staging method merely considered the EOG patterns as assistance for REM sleep scoring, not taking it as a primary sign of sleep. In our study, we found that EOG can not only capture the eye movement activity but some patterns from EEG signs. This made the sleep scoring based on EOG become available. We introduced some EOG characteristic we found in this paper and finally utilized these features to develop an EOG sleep staging method. After training and testing with 16 subjects, our method reached overall 90.82% agreement. The result proved that the EOG channel is useful on sleep staging as EEG did and supports the development on self-applicable sleep monitoring device for further application.
論文目次 摘 要 I
ABSTRACT II
誌 謝 IV
List of Figures VII
List of Tables X
Chapter 1 Introduction 1
1.1 Background 1
1.2 Visual Scoring Rules Human Sleep 2
1.3 Review of Literature 4
1.4 Motivation 5
Chapter 2 Materials and Methods 6
2.1 Subjects Description and Recording 6
2.2 Features Description and Visual Staging Rules for EOG 8
2.3 Smoothing 19
Chapter 3 Result 21
3.1 Global Performance 21
3.2 Individual Performance 27
3.3 Assessment of objective sleep measurements 29
Chapter 4 Discussion 32
Chapter 5 Conclusions and Future Works 34
Reference 35
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