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系統識別號 U0026-2606201219352900
論文名稱(中文) 基於心率變異度之自動睡眠判讀方法
論文名稱(英文) A heart-rate-variability based automatic sleep scoring method
校院名稱 成功大學
系所名稱(中) 醫學資訊研究所
系所名稱(英) Institute of Medical Informatics
學年度 100
學期 2
出版年 101
研究生(中文) 陳俊佑
研究生(英文) Chun-Yu Chen
學號 Q56994031
學位類別 碩士
語文別 中文
論文頁數 58頁
口試委員 指導教授-梁勝富
口試委員-蕭富仁
口試委員-張大緯
口試委員-潘欣泰
中文關鍵字 自動睡眠判讀方法  心電圖  心跳速率  心率變異度  睡眠週期  受測者間獨立 
英文關鍵字 automatic sleep scoring method  Electrocardiography  ECG  heart rate  heart-rate-variability  HRV  sleep cycle  subject-dependent 
學科別分類
中文摘要 睡眠,是十分重要的。然而,並不是每個人都可以擁有良好的睡眠品質。在臨床上常以多重睡眠生理記錄儀 (PSG) 來收錄病患整晚的睡眠生理訊號,並藉此訊號來觀察病患的睡眠品質。由於人工睡眠判讀十分費時且時常包含專家自身的主觀想法,因此自動睡眠判讀方法的開發便成為一件十分重要的課題。傳統的腦電圖、眼電圖、肌電圖之生理訊號組合對使用者的睡眠品質干擾較大,使得判讀結果有時無法準確反映出該使用者真正的睡眠狀況。因此在本研究中我們採用對使用者睡眠品質干擾較低的心率訊號來開發自動睡眠判讀演算法。雖然目前使用心率相關訊號來做睡眠判讀的方法還不算成熟,但它低干擾、方便收錄且又可以觀察完整睡眠週期的特性,使得它未來的發展不可限量。
由於每個人的心率特徵不盡相同,現有方法卻大多採用將受測者分群,交叉驗證的方式進行睡眠判讀,導致普遍準確度都不高。故使用心電圖作為分類特徵時,無法如腦電圖、眼電圖、肌電圖等生理訊號,訓練一個通用模組套用在每位受測者身上。故在本研究中,我們改採每位受測者間獨立訓練自己參數的模式,搭配三個特徵值來建構演算法:此三項特徵分別是平均心率、心率變異數及心率變異度中的低頻功率。本演算法在測試過15位受測者之後,得到整體判讀準確度為69.48%、清醒準確度為63.48%、淺度睡眠準確度為71.30%、深度睡眠準確度為68.06%、快速眼動期準確度為68.78%。本研究未來預期可與現有的心率量測系統如心電圖、指夾式血氧計等結合,實際應用於臨床監測與居家照護等相關領域。
英文摘要 Sleep is important to everyone. However, not everyone can acquire good sleep quality. For the diagnosis, all night polysomnographic (PSG) recordings are usually taken from the patients. The doctor needs to realize the sleep quality and quantity of them. Nevertheless, visual sleep scoring is a time consuming and subjective process. Therefore, developing an automatic sleep scoring method is a very important issue. Due to the disturbance from typical biomedical signals: EEG, EOG, and EMG recording are too huge, the sleep quality scored from those signals is not accurate enough. So our objective of this study is developing an automatic sleep scoring method which only uses the heart rate as the input signal. Although the method using HRV as the input signal is not good enough, the benefits like less disturbance, easy to use and capability of detecting sleep cycle, make it has unlimited potential.
Everyone has different heart rate features. However, most of recent studies used cross-subject concept to develop their automatic sleep scoring method, cause they have lower accuracy. According to this, we can’t use cross-subject way to treat the signal like EEG, EOG and EMG dataset. In the study, we use the concept of subject-dependent to construct our method. Using this concept and 3 features: average heart rate, variance of heart rate and HRV LF power, we have 69.48% total accuracy, 63.48% accuracy for wake, 71.30% accuracy for light sleep, 68.06% accuracy for deep sleep, and 68.78% accuracy for REM sleep. We expect this study can integrate with various heart rate signal recorder such as ECG and pulse oximeter for sleep monitoring in clinical or homecare application.
論文目次 摘要 I
ABSTRACT II
誌謝 III
目錄 V
表目錄 VII
圖目錄 VIII
第1章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 相關研究 4
1.4 論文架構 5
第2章 研究背景與原理 6
2.1 人類睡眠狀態的定義 6
2.2 心臟收縮機制 9
2.3 心電圖簡介 12
2.4 心率變異度分析 16
2.5 Pan-Tompkins 演算法 20
第3章 研究方法 24
3.1 實驗環境 24
3.2 實驗收錄 25
3.3 使用特徵 26
3.3.1 平均心跳速率與心率變異數 31
3.3.2 HRV頻域分析指標LF 32
3.4 自動睡眠判讀演算法 35
第4章 研究結果 42
4.1 PSG收錄資料分析 42
4.2 自動睡眠判讀效能分析 42
第5章 討論 48
第6章 結論與未來展望 53
參考文獻 55
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