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研究生：	葉仲文 Ye, Jhong-Wun
論文名稱：	三維脈波圖譜之小波與三次樣條內插基線消除和L-Cube脈象辨識的改良 Baseline Elimination by Wavelet and Cubic-Spline Interpolation for 3DPM and Improvement of L-Cube Pulse Mapping Recognition
指導教授：	羅錦興 Luo, Ching-Hsing
共同指導:	李宗錂 Lee, Tsung-Lin
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：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	72
中文關鍵詞：	中醫、基線消除、三維脈波圖譜、脈象辨識、指數多項式、近似
外文關鍵詞：	Chinese Medicine (CM), Baseline Elimination, Three-Dimensional Pulse Mapping (3DPM), Pulse Recognition, Exponential Polynomial Function, Approximation
相關次數：	點閱：162 下載：0
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本研究提出了一個基於小波與三次樣條內插之脈搏波基線消除的訊號處理架構，針對脈象儀所收集的資料，考慮受測者移動與脈象儀感測器的硬度等狀況，針對各個通道進行適應性的訊號處理，以利建構良好三維脈波圖譜，其研究結果顯示本架構對於不同品質的脈波訊號皆有良好的處理效果，唯較高頻之雜訊抑制效果較為有限。
本研究對於先前提出的L-Cube函數脈象辨識也進行了函數的修改、擬和方法與結果評估的改良。透過加入x-與y-方向之超平面，改良L-Cube函數在進行不對稱三維脈波圖譜擬和時的彈性度，並將形狀參數a, b以軸比率r (新的弦脈指標，相較於離心率變化更為顯著)與衰減參數σ代換，目的是希望隨擬和流程結束能同時得到脈象的參數。然而以現在的L-Cube函數仍無法妥當描述不均勻的三維脈波圖譜，因此更健全的函數亦可成為未來之研究方向。
針對擬和之目標函數與結果評估，我們放棄參考體積誤差(VE)與R2，保留均方差根(RMSE)，引入權重式均方差根(RMWSE)、旋轉角度誤差與中心點誤差，並將上述評估項正規化。透過實驗，我們得到評估這四項誤差的權重宜選為1 : 1 : 0.01 : 0。
將冷刺激實驗(Cold Pressor Test)得到的資料，以上述的誤差項權重進行新L-Cube函數分析，結果指出其弦脈指標r確實有隨冷刺激明顯上升並隨刺激移除回降的趨勢，如文獻中指出的冷刺激將造成短暫性的血壓升高，其脈象也將進而趨向中醫的弦脈。此外，由擬和結果中r的變異數亦能看出受到個體差異的影響，冷刺激實驗並非總是能順利造成脈象往弦脈趨勢發展。

This research proposed a pulse wave processing framework of baseline removal based on wavelet and cubic-spline interpolation. With the consideration of subject movement, sensor hardness and other situations, the framework processes pulse signal in each channel adaptively for good construction of the 3DPM. The result shows that the proposed framework has good performance for pulse wave signals with different qualities. However, the high-frequency suppression of this work can be improved on future research.
This study has modified the L-Cube recognition proposed previously and improved the methods of fitting and evaluation. Hyperplanes in both x- and y-direction are added into the L-Cube function for a better flexibility when describing an asymmetric 3DPM. Also, the shape parameter a and b are replaced with the axis ratio r and the decay parameter σ. As the fitting process completes, the axis ratio is found to be the new string-like index which is more sensitive than the eccentricity ε. However, the new L-Cube is still not able to describe non-uniform 3DPMs well and further research can be done on this.
On the topic of fitting method and evaluation, we abandon the volume error (VE) and R2 but keep the root mean squared error (RMSE). Besides, the weighted RMSE, rotation angle error and the center point deviation are introduced for the fitting and its evaluation. From the experiment, we have found the weights of above terms are suggested to be 1 : 1 : 0.01 : 0.
Using the weights concluded above, a cold pressor data (CPT) was analyzed with the new L-Cube function. The resultant string-like index increased as the cold stimulation applied and went back down as the of ice bag removed. This verified the work related to making blood pressure higher temporally by CPT so that the pulse condition tends to get more string-like as well. Besides, the variation of r indicates the individual differences may lead the pulse condition fail to get more string-like through the CPT.

摘要	I
Abstract	III
Acknowledgement	V
Table of Contents	IX
List of Tables	XI
List of Figures	XII
Chapter 1. Introduction	1
1 Chinese Medicine and Pulse Diagnosis	1
2 Motivations and Objectives	3
Chapter 2. Previous Studies and Methods Retrospection	5
1 Existing Baseline Elimination Methods for Pulse Waveform	5
1.1 Review of Baseline Removal Methods	5
1.2 Review of Onset Point Searching Algorithms	6
2 The Original L-Cube Function	6
Chapter 3. Baseline Elimination	10
1 Overview of Pulse Waveform Baseline Elimination Architecture	10
2 Baseline Estimation by Discrete Wavelet Transform (DWT)	10
2.1 Wavelet Transform (WT) and Multi-Resolution Analysis (MRA)	10
2.2 Discrete Wavelet Transform (DWT)	13
2.3 DWT-Based Baseline Elimination	17
3 Baseline Estimation by Cubic Spline Interpolation	19
3.1 Interpolation Techniques	19
3.2 Onset Points Detection	26
4 Pulse Waveforms with Special Phenomenon	27
4.1 Merging Sub-Peaks	27
4.2 Pulse Waveforms with Negative Direction	27
Chapter 4. Improved L-Cube Pulse Mapping Method	28
1 The Modification of L-Cube Function	28
1.1 The Center Point (xc, yc) and the Hyperplane Parameters kx, ky	28
1.2 The Eccentricity ε, the axis ratio r and the decay parameter σ	30
2 Degree of Freedom and The Feasible Set of L-Cube Function	34
2.1 Dependency of L-Cube Parameters	35
2.2 Feasible Set for Parameters	37
3 Pulse Information from New L-Cube Parameters	38
3.1 The String-Like Index	38
3.2 The Heart Force in Artery	42
Chapter 5. Experimental Data and Analysis Methods	44
1 Experimental Data	44
2 The Procedures of the 3DPM Parameterization	45
2.1 Advanced Surface Fitting Process	46
2.2 Algorithms for Defining Reference Parameter	48
2.3 Solution to Constrained Optimization Problems	52
2.4 The Active Set Algorithm	54
3 Model Adequacy of the L-Cube Function	56
Chapter 6. Results	58
1 Proposed Waveform Baseline Removal Methods	58
2 Comparison of Previous and New L-Cube Fitting Results	59
3 Improvement of Advanced Fitting Process	61
3.1 The Root Mean Weighted Squared Error Term	61
3.2 The Rotation Angle Error Term	62
3.3 The Center Point Error Term	64
4 The String-Like Index	65
Chapter 7. Discussion and Conclusion	68
Reference	70
                                    

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