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系統識別號 U0026-2008201014205500
論文名稱(中文) 應用於睡眠-清醒判讀之適應性活動紀錄分析技術
論文名稱(英文) An Adaptive Actigraphy Analysis Method for Sleep-Wake Identification
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 98
學期 2
出版年 99
研究生(中文) 劉懿哲
研究生(英文) Yi-Che Liu
學號 p7697120
學位類別 碩士
語文別 英文
論文頁數 60頁
口試委員 指導教授-梁勝富
口試委員-蕭富仁
口試委員-林志生
口試委員-張大緯
口試委員-鍾翊方
中文關鍵字 加速規  活動紀錄器  睡眠診斷  睡眠-清醒判斷  失眠 
英文關鍵字 accelerometer  actigraphy  sleep diagnosis  sleep-wake scoring  insomnia 
學科別分類
中文摘要 睡眠是生命中相當重要的一部分,人一生中會用掉三分之一的生命在睡眠上,但睡眠障礙是人們最常遇到的生理問題之一,其中失眠為睡眠障礙中比例最多的一種睡眠問題,然而患有慢性失眠人口在台灣為 21.8%。另外,經過統計約有 60萬美國人在一年中有曾經失眠或長期失眠。因此,我們發展了一個手腕式活動紀錄器來偵測睡眠活動達到睡眠獲得客觀的睡眠品值之評估,因此我們也提出一個新的演算法使用於此自製的活動紀錄系統,該演算法將採用自適應視窗以適應不同的睡眠效率的受試者來平衡睡眠與清醒之間的準確率,我們錄製了35個健康的青年受試者和5個有失眠的受試者的整晚約8小時的睡眠活動訊號,並使用這些數據透過我們的演算法計算清醒-睡眠結果與多通道生理監控儀(PSG)的清醒-睡眠判讀結果做比較,結果將可以達到 91 %的整體準確度, 92 %的靈敏度,80 %的明確性,97 %的睡眠預測值與54 %的清醒預測值,與其他已提出的活動紀錄做清醒-睡眠分析的方法相比整體提升了不少的準確度。
英文摘要 Sleep is an integral part of life, person will have spent one-third of the life for sleep generally, but sleep disorder is people one of the most frequently encountered of physiological problems, and insomnia is the most part of sleep disorder, the prevalence of chronic insomnia in Taiwan was 21.8%. After statistics, about 60 million Americans in a year have insomnia frequently or for extended periods of time. So we develop a wrist actigraphy and we propose a new algorithm for self-made actigraphy, the algorithm is use adaptive window size to fit different sleep efficiency and balance performance between sensitivity and specificity, we recorded 35 health young subject and 5 insomnia subject all night about 8 hours actigraphy signal for measure our algorithm with polysomnograph (PSG), the result of our algorithm is reach 91 % in overall accuracy, 92 % in sensitivity, 80 % in specificity, 97 % in predictive value for sleep and 54 % in predictive value for wake, compare with other method presented previously will higher in all of the performance.
論文目次 摘要 I
Abstract II
Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Relate Works 2
1.3 Motivation 5
1.4 Object 5
Chapter 2 System Architecture 6
2.1 System Block Diagram 6
2.2 Three-Axis Accelerometer 6
2.3 High-Pass Filter 7
2.4 Analog to Digital Converter 10
2.5 Micro-Controller 11
2.6 System Firmware 12
Chapter 3 Method 14
3.1 Basic Idea 14
3.2 Experiment Evaluation 15
3.3 Data Acquire and Preprocess 16
3.4 Feature Extraction 17
3.4.1 Peak to Peak Interval (PP-interval) 17
3.4.2 Maximum 19
3.5 Movement Density 19
3.6 K-means cluster 20
3.7 Metrics for Evaluation & Actigraphy Algorithm 21
3.7.1 Metrics for Evaluation 21
3.7.2 Automatic Sleep-Wake Staging Algorithm 22
Chapter 4 Results 28
4.1 Data Analysis with PSG 28
4.2 Performance of Sleep-Wake Staging 28
4.3 Performance of Sleep-Wake Staging with Excluded Exception Data 37
4.4 Sleep Efficiency Analysis 46
Chapter 5 Discussion 52
Chapter 6 Conclusion and future work 56
Reference 57
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