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研究生： 何竹軒
Ho, Jhu-Syuan
論文名稱： 以時頻分析血壓訊號
Time-Frequency Analysis of Arterial Blood Pressure Signals
指導教授： 李國君
Lee, Gwo Giun
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 88
中文關鍵詞： 特徵萃取 、脈診儀訊號 、時頻分析 、血管壁彈性估測 、脈象分類 、心跳變異率分析 、訊號比對 、健康看護系統 、雲端資料庫
外文關鍵詞： feature extraction, arterial blood pressure (ABP) signals, time-frequency analysis, arterial wall elasticity estimation, pulse classification, heart rate variability (HRV) analysis, signal matching, health-care system, health-cloud
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    • 本論文透過時頻分析，在中醫脈診儀訊號中萃取各種有用的生理資訊，特別是在動脈管壁彈性的估測上，應用了連續小波轉換中的尺度參數來將脈診訊號的寬度進行量化，並將其資訊運用在脈象的分類上。在心跳變異率的分析上，運用時頻分析來解決了傳統頻率分析中無法得到的確切時點資訊，因此可以立即在心跳變異率出現異常時警告使用者。最後，更可進一步的與雲端資料庫中的生理訊號，例如西醫的心電圖訊號來進行比對，將中西方所提供的醫學資訊結合起來，建立一個健康看護的系統。

    In this thesis, time-frequency analysis is used to extract variously physiological information from arterial blood pressure (ABP) signals. Especially on the arterial wall elasticity estimation, we use the scale parameter of continuous wavelet transform to quantify the width of systolic complex, which can be used for pulse classification. In heart rate variability (HRV) analysis, we use time-frequency analysis to obtain the locality information of time domain, which Fourier transform can’t know. Therefore, we can immediately warn users of abnormal heart rate variability. Last, we also can match the ABP signal with other vital sign signals in the health-cloud like electrocardiography. So that we can combine the medical information both Chinese medicine and Western medicine and apply on health-care system.

    摘 要 i Abstract ii 誌 謝 iii Table of Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Background Information 2 1.2.1 Arterial Blood Pressure Signals 2 1.2.2 Heart Rate Variability 5 1.3 Motivation 6 1.4 Organization of this Thesis 7 Chapter 2 Surveys of Related Works in the Literatures 8 2.1 Joint Time-Frequency Analysis 8 2.1.1 Short-Time Fourier Transform 8 2.1.2 Continuous Wavelet Transform 10 2.2 Heart Rate Variability Analysis 13 Chapter 3 Proposed Algorithms 17 3.1 Feature Extraction on Arterial Blood Pressure Signals 17 3.1.1 Block Diagram 17 3.1.2 Wavelet Analysis 19 3.1.3 Coefficient Selection 20 3.1.3.1 Frequency Analysis 21 3.1.3.2 Energy Analysis 22 3.1.4 Arterial Wall Elasticity Estimation and Pulse Classification 24 3.1.5 Position Detection 30 3.1.5.1 P Peak Detection 30 3.1.5.2 U Point Detection 31 3.1.5.3 D, T Peak Detection 32 3.1.5.4 V Point Detection 33 3.1.6 Amplitude & Duration Estimation and Medical Study 34 3.2 Heart Rate Variability Analysis 35 3.3 Signals Matching between ABP and ECG Signals 39 Chapter 4 Experimental Results 42 4.1 Data Specification 42 4.2 Feature Extraction for Arterial Blood Pressure Signals 42 4.2.1 Mother Wavelet Selection 42 4.2.2 Arterial Wall Elasticity Estimation and Pulse Classification 45 4.2.3 Position Detection 55 4.2.3.1 Subjective Experimental Results 55 4.2.3.2 Objective Experimental Results 58 4.2.3.3 Comparisons 64 4.2.4 Amplitude and Duration Estimation 64 4.3 Heart Rate Variability Analysis 71 4.4 Signals Matching between ABP and ECG Signals 81 Chapter 5 Conclusions and Future Works 84 5.1 Conclusions 84 5.2 Future Works 85 References 86

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