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研究生(外文):LIU, HAN-CHI
論文名稱(外文):Dynamic Measurement of Forehead Peripheral Arterial Oxygen Saturation (SpO2) Based on Machine Learning
外文關鍵詞:Oxygen saturation (SpO2)Photoplethysmography (PPG)Pulse waveSupport Vector Machine (SVM)
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血氧飽和濃度(Oxygen Saturation, SpO2)的應用包含了醫療、運動休閒以及睡眠品質檢測等,是評估身體健康狀況的重要參數。目前市面上常見的脈搏血氧儀多為指夾式,其量測期間容易受到移動干擾及環境光等因素的影響,造成數值失準,同時也影響到受測者的活動性。本研究旨在製作一套前額式脈搏血氧濃度儀,用於測量人體的前額PPG訊號,並提高動態活動時SpO2的準確度。本系統採用MAX30102晶片組成陣列式感測器,可以在量測時選擇PPG訊號較佳的位置進行量測。為檢測出受干擾之PPG訊號,本研究依據SpO2的數值、相對誤差及脈搏波之形態分析,作為標記區段PPG訊號品質的標準,最後分別從紅光(Red)及紅外光(IR)PPG訊號中,提取出12種特徵輸入至SVM進行PPG訊號品質的分類。

The applications of Oxygen Saturation (SpO2) include medical treatment, sports, leisure and sleep monitoring. It plays an importance role in assessing physical heath. At present, most of the pulse oximeters on the market are finger-type which are easily affected by motion artifact and ambient light during the measurement. These corruptions reduce SpO2 accuracy, and this sensor probe also limits the user’s mobility. In this study, a forehead pulse oximeter which measured forehead Photoplethysmographic (PPG) signal with a higher accuracy for the SpO2 measurement is developed. This system uses the integrated chip (MAX30102) to build an array-type multi-sensor probe, which is able to select a better position for measuring the SpO2. In order to detect the corrupted PPG signal, we use SpO2 value, relative error and PPG wave morphology as the standard for labeling the quality of PPG signal. Finally, 12 features are extracted separately from red and infrared (IR) PPG signals for training and testing trials. Then the SVM is employed to classify the quality of PPG signal.
We have invited 20 volunteers to participate in this study. In the experiment, a series of static and dynamic measurements are performed. The results of SVM-based classification show that the average accuracy, specificity, sensitivity and precision are respectively 89.90%, 87.27%, 91.82%, and 90.77%. Moreover, the error and standard deviation of SpO2 have been reduced effectively, which indicate that the accuracy of SpO2 during dynamic measurement can be improved, thus enhancing the practicality of this work.

摘 要 i
誌 謝 v
目 錄 vi
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻探討 2
1.3 研究方法與目的 4
1.4 論文架構 4
第二章 背景知識 6
2.1 光體積變化描記圖 6
2.1.1 測量原理 6
2.1.2 脈搏波 7
2.1.3 PPG感測器 8
2.2 血氧飽和濃度 9
2.2.1 定義 9
2.2.2 量測及估算方式 9
2.3 影響訊號品質的因素 11
2.3.1 線性支持向量機 14
2.3.2 非線性支持向量機 16
第三章 前額式脈搏血氧量測系統 18
3.1 系統架構 18
3.2 硬體元件 19
3.2.1 微控制器 19
3.2.2 脈搏血氧及心律感測器 20
3.2.3 三軸加速度計 21
3.2.4 藍芽模組 21
3.3 硬體架構及實體 22
3.4 軟、韌體架構 23
3.4.1 MCU主程式執行流程 23
3.4.2 MCU中斷副程式流程 24
3.5 數位訊號處理之執行流程 26
3.5.1 訊號反向(Signal Inversion) 27
3.5.2 低通濾波器(Low-pass filter) 27
3.5.3 高通濾波器(High-pass filter) 28
3.6 圖形化使用者介面 30
第四章 資料分析 31
4.1 波形分割及SpO2之估算 31
4.1.1 脈搏波分割(Pulse Wave Segment) 31
4.1.2 血氧飽和濃度(SpO2)的估算 34
4.2 PPG訊號之標記 36
4.2.1 脈搏波之形態特徵 36
4.2.2 脈搏波品質檢測 38
4.2.3 PPG區段訊號品質標記方法 42
4.3 PPG訊號之特徵參數選取 44
第五章 實驗方法 48
5.1 測量方法 48
5.1.1 測量裝置 48
5.1.2 最佳位置測量 49
5.1.3 靜態測量 52
5.1.4 動態測量 53
5.2 SVM分類器 55
5.3 評估方法 57
第六章 實驗結果 59
6.1 資料數量及標記結果 59
6.2 實驗一:最佳參數找尋 60
6.3 實驗二:SVM效能評估 62
6.3.1 分類結果之效能 62
6.3.2 SpO2之變化 64
6.4 實驗三:SVM以靜態資料估測動態資料 72
第七章 討論與結論 74
7.1 SVM分類結果之討論 74
7.2 結論 77
7.3 未來展望 78
參考文獻 79

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