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研究生:劉哲宏
研究生(外文):Je-Hung Liu
論文名稱:基於模糊決策之基因量化策略設計於即時性小波轉換式心電圖資料壓縮系統
論文名稱(外文):Fuzzy Decision-Based Genetic Quantization Scheme Design for Real-Time Wavelet-Based ECG Data Compression System
指導教授:洪金車
指導教授(外文):King-Chu Hung
學位類別:博士
校院名稱:國立高雄第一科技大學
系所名稱:工學院工程科技博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:138
中文關鍵詞:改良型階層樹集合分割編解碼演算法模糊決策模式基因演算法可逆式轉換法非遞迴式離散週期性小波轉換心電圖壓縮
外文關鍵詞:fuzzy decision-makinggenetic algorithmNRDPWTReversible transformationECG data compressionMSPIHT
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即時性多導程心電圖資料傳輸與紀錄對預防性醫學和老年照護相當重要,其中以無線身體感測網路裝置 (Wireless body sensor networks簡稱WBSN)是最佳的實施方式,因為方便且不妨礙日常生活活動。然而,為了輕巧方便的設計,WBSN存在待機時間過短的問題,因為無線資料傳輸很耗電能。克服耗能問題目前最有效的方法:1)直接發射被壓縮的感測訊號(emerging compressed sensing (CS) signal),2)傳送編碼後的量化資料。這兩種皆屬轉換式(transform-based)心電圖資料壓縮法,前者將量化係數直接植入轉換函數中,此法的量化尺度不可調,無法滿足醫學診療應用需維持重建訊號品質穩定的要求。後者藉由一品質保證機制可選取最佳量化係數來壓縮轉換後的資料,此法可維持重建訊號品質的穩定,但費時和耗硬體資源。在轉換式心電圖資料壓縮法中則以基於小波轉換壓縮法可得最佳壓縮效能。
轉換式資料壓縮法的量化處理是在頻域中,賦予每一頻率個別的量化係數,而失真度則須回到時域中量測,此流程是造成重建品質難以控制且保證品質相當費時的主要原因。為克服此困難,我們結合基因演算法(genetic algorithm)與三維曲線契合(3D curve fitting)技術研發出一系統性量化策略設計法,此法可以單一量化參數(QF)得到線性的失真度與壓縮比率(CR)關係。此線性特性不僅可有效提升整體壓縮效能,亦提供建立失真度線性評估模式(linear prediction model)之基礎。但由於基因演算法存在資料相依(data dependence)特性,本論文提出以模糊多準則決策法則來降低資料相依性,我們從MIT-BIH和 PTB兩種特性和浮動區間皆迥異的心電圖資料庫中選出4組訊號做為訓練資料,再建立模糊多準則決策模式,並以模糊語意變數(Linguistic variables)衡量每個準則權重及選擇方案之評比選擇出最佳的量化策略。壓縮系統的轉換模式則採用可逆式四捨五入非遞迴式小波轉換(RRO-NRDPWT)法以求得最短浮動區間的無失真轉換系數,最後提出一改良型階層樹集合分割編解碼演算法(MSPIHT)對量化後係數進行無失真壓縮。MSPIHT是採位元平面(Bit-plane)處理方式,適合硬體實現的即時編碼。實驗結果採用二種效能評估方法:均方根誤差百分比(PRD)及重建訊號的視覺觀測法,本系統可獲得比SPIHT壓縮方法更佳的壓縮效能和時間。
Electrocardiogram (ECG) signal analysis is a noninvasive modality widely used for heart disease diagnosis. With high sensitivity, real-time reporting the use of a multi-lead ECG signal is a necessary measure in prevention-oriented healthcare and elderly care. An efficient option in this regard would be equipping patients with a wearable and wireless body sensor network (WBSN). However, WBSN has the problem of short stand-by. There are two methods commonly used for solving this problem: 1) emerging compressed sensing signal, and 2) transmitting transformed encoded and quantized data. Both methods involve transform-based ECG data compression. The former directly plugs the quantization scale into transform coefficients. Since quantization scales cannot be adjusted, this method does not satisfy the requirement of guaranteeing reconstruction quantity. The latter chooses the best quantization scale by a quality guarantee mecha-nism (QGM), and can maintain reconstruction quality stability, but at a time-consuming cost and complex hardware. In the transform domain, the wavelet-based compression method has the best compression performance
The transform-based quantization process is part of the frequency domain, rendering a coefficient to each sub-band, and the reconstruction quality is processed in the time domain. In regard to this method, we proposed a non-linear quantization algorithm by adjusting the single quantization factor (QF) based on genetic algorithm and curve fit-ting. The goal of this algorithm is to maintain a linear relation between the distortion and compression ratio (CR). This linear relationship is not only based on improving the com-pression performance of the whole system, but also building a linear distortion predic-tion model. In this dissertation, a fuzzy decision-making method is used for minimizing the data dependency effect; for this reason, the training data should be as diverse as possible. To this end, four training datasets (TD#1, TD#2, TD#3 and TD#4) were built. A fuzzy decision-making method under multiple criteria consideration is needed to in-tegrate various linguistic assessments and weights to evaluate and determine the best selection. With minimum dynamic range (MDR), we proposed a reversible round-off non-recursive wavelet transform in the transform section. Finally, a modified set parti-tioning in hierarchical trees (MSPIHT) algorithm is proposed in the lossless coding. MSPIHT uses the bit-plane and is a suitable method to implement on hardware. The performance evaluation is based on the percentage root-mean-square difference (PRD) and visual inspection of the reconstructed signals. The experimental results show that our proposed method can obtain superior compression performance compared to SPIHT.
CONTENTS
                      
Chinese Abstract---------------------------------------------------------i
English Abstract---------------------------------------------------------iii
Acknowledgements---------------------------------------------------------vi
Contents---------------------------------------------------------------------------vii
List of Tables---------------------------------------------------------------------x
List of Figures----------------------------------------------------------------------xi
List of Abbreviations------------------------------------------------------------xv
CHAPTER 1 INTRODUCTION--------------------------------------------------1
1.1 Background--------------------------------------------------------1
1.2 Motivation----------------------------------------------------2
1.3 The Objectives of This Study--------------------------------------5
1.4 Organization of This Dissertation----------------------------------7
CHAPTER 2 RELATED WORKS-----------------------------------------------8
2.1 Concept of Orthonormal Functional Basis------------------------8
2.2 The 1-D Discrete Periodized Wavelet Transform------------10
2.3 The 1-D Non-Recursive Discrete Periodized Wavelet Trans-form---------------------------------------------------------------18
2.4 The theorem of reversible round-off linear transformation--27
2.5 Set partitioning in Hierarchical Trees Algorithm--------------34
2.6 Modified Set Partitioning in Hierarchical Trees-----------36
2.7 Experimental Results-------------------------------------------45
2.8 The Test Signals of MIT-BIH Arrhythmia, ST change and PTB Database------------------------------------------------------47
CHAPTER 3 WAVELET-BASED ECG DATA COMPRESSION SYS-TEM----------------------------------------------------------------49
3.1 RRO-NRDPWT for WLG Effect Elimination------------------49
3.2 Nonlinear Quantization Scheme with Linear Distortion Characteristic---------------------------------------------------53
3.3 Linear Prediction Algorithm for High Efficient Error Control Loop-----------------------------------------------------------------58
3.4 Experimental Results-----------------------------------------------62
CHAPTER 4 GA-BASED WAVELET COEFFICIENT QUANTIZATION FOR LINEAR DISTORTION ECG DATA COMPRES-SION-----------------------------------------------------------------64
4.1 GA-Based Quantization Scheme Design for Linear Quality Control Scheme----------------------------------------------64
4.2 Experimental Results----------------------------------------------71
4.3 Discussion & Conclusions-----------------------------------------81
CHAPTER 5 A LOW COMPLEXITY QUALITY CONTROL SYS-TEM-----------------------------------------------------------------87
5.1 Error Model Estimation--------------------------------------------87
5.2 Simplify the PRD----------------------------------------------91
5.3 Discussion & Conclusions-----------------------------------------94
CHAPTER 6 DECISION-MAKING METHOD --------------------95
6.1 Fuzzy Sets Method -------------------------------------------------95
6.2 Multiple Criteria Decision-making Method--------------------95
6.3 Experimental Results---------------------------------------------100
CHAPTER 7 DISCUSSIONS AND CONCLUSIONS---------------------104
APPENDIX-----------------------------------------------------------------------107
REFERENCES------------------------------------------------------------------110
PUBLICATION LISTS--------------------------------------------------------118
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