基於階層迴歸方法運用光體積描記訊號與心電圖估測血壓與脈波傳導速度

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姓名

李御銓(Yu-Chuan Li) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於階層迴歸方法運用光體積描記訊號與心電圖估測血壓與脈波傳導速度
(Estimation of Blood Pressure and Pulse Wave Velocity from Photoplethysmography and Electrocardiography Using Hierarchy Regression)

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至系統瀏覽論文 (2027-8-31以後開放)

摘要(中)

血壓以及脈波傳導速率為心血管疾病重要的指標，血壓量測上多仰賴脈壓式血壓計，而脈波傳導速率則是需要在醫院量測，大多儀器是以壓脈帶方式量測。近年來，利用穿戴式裝置量測生理訊號，推算血壓與脈波傳導速率技術逐漸成熟，具有可長時間監測的優點以及對遠距醫療有所幫助。本論文利用心電訊號及光體積描記訊號來估測血壓和脈波傳導速率。從裝置取得手指及手腕心電訊號與光體積描記訊號後，對波型進行前處理，去除60Hz市電雜訊及基線飄移，並對訊號不良的波型進行初步的過濾，透過特徵萃取程序從心電訊號與光體積描記訊號取得特徵，並對光體積描記訊號進行波型拆解，將波型拆解成5個高斯成份波，並取其振幅、寬度與中心位置當作特徵。在資料處理的階段，篩選可用的特徵與波型段，並對不同血壓及脈波傳導速率的區間補足，以維持訓練資料集區間的平衡。而基於不同區間的血壓或是脈波傳導速率所看重的特徵不一定相同的觀察，因此提出了分層迴歸的方法，讓估測結果可以更加準確。使用手腕訊號特徵估測脈波傳導速率，演算法使用極限梯度提升(XGBoost)並直接做全域迴歸，女性均方根誤差為183.73(cm/s)，男性均方根誤差為188.80(cm/s)，如果採用先分類再迴歸，可以使得女性均方根誤差進步到149.72(cm/s)，男性均方根誤差進步到160.15(cm/s)。而手指訊號特徵估測血壓，參考了國際量測血壓標準，以符合區間分布數量要求的85人255筆量測資料作為測試集。使用神經網路做全域迴歸，收縮壓均方根誤差為10.71(mmHg)，若使用分層迴歸的技術，收縮壓均方根誤差可達到9.79(mmHg)。

摘要(英)

Blood pressure and pulse wave velocity are important indicators of cardiovascular diseases. Blood pressure measurement mostly relies on the mercury sphygmomanometer and pulse wave velocity usually needs to be measured in hospitals. Most instruments measure pulse wave velocity by means of pressure pulse band. In recent years, the technology of measuring physiological signals by using wearable devices becomes popular, which has the advantages of long-term monitoring and is helpful for remote medical treatment. In this thesis, Electrocardiography (ECG) signals and photoplethysmography (PPG) signals are used to estimate blood pressure and pulse wave velocity, and the feature properties of blood pressure or pulse wave velocity in different zones may not be the same, so a hierarchical regression method is proposed to make the estimation results more accurate. The PPG signal from wrist was used to estimate the pulse wave velocity. We used eXtreme Gradient Boosting (XGBoost) for global regression. The root mean square estimation error (RMSE) for female and male was 183.73(cm/s), and 188.80(cm/s). The root mean square error can be improved to 149.72(cm/s) for female and 160.15(cm/s) for male if we applied classification before regression. The PPG and ECG signals from finger were used to estimate blood pressure according to the international standard of blood pressure measurement. The test set were composed of 255 measurements from 85 people which met the requirements of BP distribution. The root mean square error of systolic blood pressure was 10.71(mmHg) when neural network was adopted for global regression. The root mean square error of systolic blood pressure was 9.79(mmHg) when residual neural network-based hierarchical regression was used.

關鍵字(中)

★ 血壓估測
★ 脈波傳導速率估測
★ 心電圖
★ 光體積描記訊號

關鍵字(英)

★ Blood Pressure Estimation
★ Pulse Wave Velocity Estimation
★ Electrocardiography
★ Photoplethysmography

論文目次

摘要 i
Abstract ii
表目錄 vi
圖目錄 ix
第一章緒論 1
1.1 研究動機 1
1.2 研究方法 1
1.3 論文組織 2
第二章生理訊號 3
2.1 脈波傳導速率 (Pulse Wave Velocity, PWV) 3
2.2 舒張壓(Systolic Blood Pressure, SBP)與收縮壓(Diastolic Pressure, DBP) 4
2.3 光體積描記訊號 (Photoplethysmography, PPG) 5
2.4 心電圖 (Electrocardiogram, ECG) 6
第三章特徵萃取 7
3.1 流程圖 7
3.2 前處理 (Preprocessing) 9
3.3 預篩選 (Pre-screening) 10
3.3.1 飽和訊號檢測 (Saturated Signal Detection) 10
3.3.2 異常峰波檢測 (Abnormal Peak Detection) 10
3.4 特徵擷取(Feature Extraction) 12
3.4.1 光體積描記訊號特徵擷取 12
3.4.2 心電圖特徵擷取 12
3.5 訊號品質檢測(Signal Quality Index) 14
3.6 加權式波型拆解(Weighted Pulse Decomposition) 15
3.7 加權式波型拆解品質檢測 (Weighted Pulse Decomposition Signal Quality Index) 17
3.8 手腕與手指訊號比較 18
第四章脈波傳導速率估測及結果 19
4.1 資料處理 19
4.1.1 資料與特徵可靠度檢查 (Data and Feature Reliability Check) 20
4.1.2 特徵擴充(Feature Combination) 22
4.1.3 取中位數(Median Selection) 24
4.1.4 資料集切割(Data Set Segmentation) 24
4.2 演算法 27
4.2.1 線性迴歸 (Linear Regression) 27
4.2.2 極限梯度提升 (eXtreme Gradient Boosting, XGBoost) 28
4.3 全域迴歸模型 29
4.3.1 Multiple linear regression 29
4.3.2 極限梯度提升 (eXtreme Gradient Boosting, XGboost) 32
4.4 Hierarchical Regression Model 33
4.5 結果比較 42
4.6 PWV量測裝置驗證說明 45
4.7 總結 46
第五章血壓估測演算法及結果 47
5.1 資料處理 47
5.1.1 血壓內插以及波型分割(Blood Pressure Interpolation and Waveform Division) 48
5.1.2 計算K值與多尺度熵計算(Calculate K Value and Multiscale Entropy) 48
5.1.3 特徵擴充(Feature Combination) 49
5.1.4 取中位數(Median Selection) 51
5.1.5 資料可靠度檢查(Data Reliability Check) 51
5.1.6 資料集切割(Data Set Segmentation) 53
5.2 演算法 61
5.2.1 標準神經網路 (Neural Network, NN) 61
5.2.2 卷積神經網路(Convolutional Neural Network, CNN) 62
5.2.3 短時距傅立葉轉換(Short-Time Fourier Transform) 63
5.3 全域迴歸模型(General Model) 66
5.3.1 學習速率下降因子(Learning Rate Drop Factor) 66
5.3.2 批量大小(Batch Size) 69
5.3.3 雙輸出模型(Two Output Net) 74
5.3.4 單輸出模型(One Output Net) 77
5.3.5 殘餘網路(Residual Net) 80
5.3.6 一維卷積神經網路架構 87
5.3.7 二維卷積神經網路架構 89
5.4 組合迴歸模型(Combining Model) 93
5.4.1 使用全部特徵 94
5.4.2 使用部分特徵 102
5.4.3 General Model之結果選擇 127
5.5 加入驗證資料集 133
5.5.1 全域迴歸模型(General Model) 133
5.5.2 組合迴歸模型(Combining Model) 136
5.6 結果比較 144
5.6.1 本論文所有模型的測試估測效能 144
5.6.2 本論文結果與其他論文之比較 147
5.7 總結 151
第六章結論與未來展望 152
參考資料 153

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指導教授

蔡佩芸(Pei-Yun Tsai)

審核日期

2022-8-10

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