基於手指光電容積描記法與心電訊號之血壓估測

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姓名

郭嘉威(Jia-Wei Guo) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於手指光電容積描記法與心電訊號之血壓估測
(Estimation of Blood Pressure Using Photoplethysmography and Electrocardiogram)

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至系統瀏覽論文 (2026-9-14以後開放)

摘要(中)

血壓為心血管疾病最重要的指標之一，但傳統量測上多仰賴脈壓式血壓計，量測過程會有明顯的壓迫感，大部分的人在醫院進行的量測也容易受到白袍效應的影響，脈壓式血壓計由於有充氣加壓的運作而無法做到血壓的長時間偵測，近年來利用穿戴式裝置實現非侵入式的血壓量測頗受重視，具有可長時間的監測的優點，對遠距醫療與有所助益。本論文利用心電訊號及光容積訊號來估測血壓。從指式裝置收集了心電訊號與光容積訊號後，先對波型進行了前處理，去除60Hz市電雜訊及基線飄移，並設置預篩選機制對訊號不良的波型進行初步的過濾，透過特徵擷取在光容積訊號及心電訊號波型中取出跟血壓相關的特徵，像是心電訊號的R、S、T點與光容積訊號、其一階及二階微分波型重要資訊區間的極大值與即小值，針對ECG的R peak標記之特徵演算法在MIT-BIH資料庫對超過10萬個心跳測試，準確率高達99.46%，在取出特徵後，另外也將光容積波型透過內點法加權後拆解成5個高斯成份波，並取出其振幅、寬度及中心位置來當作估測血壓特徵，在資料處理階段，先篩選可用的特徵及波型段，並進行各區間資料的超取，將血壓資料不足的區間補足，維持資料集區間的平衡，我們比較了四種機器學習的方法，並利用特徵擴充、多輸出、卷積神經網路技術，逐步改善及優化我們的血壓估測模型，並提出將傳統的取點特徵、波型拆解特徵及深度學習網路結合，發展出多層感知器結合卷積神經網路的架構，參考了國際量測血壓的標準，以符合區間分布數量要求的85人255筆量測作為測試集，針對量測數據進行估測實驗，估測結果在收縮壓上達到均方根誤差10.23(mmHg)及舒張壓8.22(mmHg)的準確率，跟已發表文獻做比較，在單一血壓量測數據對應1分鐘內的量測訊號與未校正的實驗設定下，我們的效能明顯優於其他篇文獻。

摘要(英)

Blood pressure is one of the most important metrics for cardiovascular disease diagnosis and treatment. The conventional measurement using cuff is not convenient and comfortable. In addition, the so-called white coat effect may influence the measurement result. Recently, the wearable devices that use non-invasive approach-es to obtain long-term monitoring of health conditions attract much attention. The wearable devices also helps to realize diagnosis and treatment of telemedicine. In this thesis, we use Electrocardiography (ECG) signal and Photoplethysmography (PPG) signal from a finger device to estimate blood pressure. In the preprocessing phase, the 60 Hz interference and baseline drift are removed from the signals, and bad-quality waveforms are eliminated by a pre-screening function. Then, the feature extraction function applies which extracts the blood pressure related features such as R, S, T peaks of ECG and the extrema of PPG as well as of the first and second deriva-tive PPG signals. Our feature extraction method of ECG R peak is tested for over 100,000 beats in MIT-BIH database, and it finally shows 99.46% accuracy. The weighted waveform decomposition technique is utilized to decompose the PPG pulse into five Gaussian component waves. Their amplitudes, widths and peak positions are also employed as our features. In the data processing phase, we screen out outliers of waveforms and features, calculate the median of feature values. The random over-sampling strategy is adopted to balance the training data set in different regions. Fi-nally, four machine learning methods are used and compared. The feature expansion scheme also shows performance improvement. Multi-layer perceptron (MLP) and convolutional neural network (CNN) are well integrated to enhance the regression model of BP. Our testing set contains 85 subjects and 255 records with distribution satisfying the AAMI requirements. The estimation performance achieves root mean square error of 10.23 (mmHg) in systolic blood pressure and 8.22 (mmHg) in diastolic blood pressure without subject-based correction. Compared to the conventional work in a similar testing condition, better estimation results are achieved.

關鍵字(中)

★ 光電容積
★ 心電
★ 血壓

關鍵字(英)

論文目次

內容
摘要 i
Abstract ii
圖目錄 v
表目錄 viii
第一章緒論 1
1.1 研究動機 1
1.2 文獻探討 1
1.3 研究方法 3
1.4 論文組織 3
第二章訊號及血壓介紹 5
2.1 舒張壓及收縮壓 5
2.2 光容積波(Photoplethysmogrtaphy, PPG) 6
2.3 心電圖 (Electrocardiogram, ECG) 7
第三章特徵萃取 8
3.1 流程圖 8
3.2 前處理 (Preprocessing) 9
3.2.1 移除基線飄移 (Removing baseline wandering) 10
3.2.2 移除60赫茲干擾 (Removing 60Hz interference) 11
3.3 預篩檢 (Pre-screening) 12
3.4 光容積波及心電圖特徵擷取(Feature extraction) 15
3.4.1 心電圖特徵擷取 15
3.4.2 光容積波特徵擷取 29
3.4.3 訊號品質檢測(Signal Quality Check) 30
3.4.4 加權式波型拆解(Weighted Pulse Decomposition (WPD)) 33
3.4.5 加權式波型拆解品質檢定(Weighted Pulse Decomposition (WPD) signal quality index) 36
第四章資料處理 37
4.1 波型分割及血壓內插(Waveform segmentation and blood pressure interpolation) 37
4.2 振幅平均及多尺度熵計算(Amplitude average and multiscale entropy calculation) 39
4.3 特徵擴充(Feature combination) 41
4.4 取中位數(Median selection) 43
4.5 資料可靠性檢查(Data reliability check) 43
4.5.1 AAMI規範 44
4.5.2 根據資料分布去除離群值 45
4.6 資料集分割(Data set partition) 46
4.6.1 訓練集資料 47
4.6.2 測試資料集 47
4.6.3 驗證資料集 49
第五章血壓估測演算法及結果 50
5.1 演算法 50
5.1.1 線性迴歸(Linear Regression) 50
5.1.2 隨機森林(Random Forest) 51
5.1.3 極限梯度提升(eXtreme Gradient Boosting) 52
5.1.4 人工神經網路(Artificial Neural Network) 53
5.2 演算法演進 54
5.3 各演算法比較 55
5.4 特徵擴充(Feature combination) 59
5.5 雙輸出神經網路(Two output regression) 62
5.6 卷積神經網路 64
5.7 結果比較 66
第六章結論 69
參考資料 70

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指導教授

蔡佩芸

審核日期

2021-9-13

推文