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研究生:劉省宏
研究生(外文):Shing-Hong Liu
論文名稱:基於模糊邏輯理論之非侵入式的血壓和血管順應性測量技術
論文名稱(外文):Fuzzy-logic-based for noninvasive blood pressure and vessel compliance measurement techniques
指導教授:林進燈林進燈引用關係
指導教授(外文):Chin-Teng Lin
學位類別:博士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:116
中文關鍵詞:血壓模糊邏輯順應性壓振式壓張計
外文關鍵詞:blood pressurefuzzy logiccomplianceoscillometrytonometer
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本論文主要目的在針對非侵入式的壓振式血壓測量和連續性血壓測量的準確問題,加以研究分析,並從測量系統中評估出血管之順應性,經由生理訊號的模擬和臨床測量比對,進行性能驗證及評估,證實本文所建構的測量系統包含演算法和控制器是優於現有的測量系統。
在非侵入性的血壓測量分為斷續和連續兩種方法,在斷續方法上,對於有心血管疾病的患者,壓振式測量法的穩健性低於聽診式測量法,而且只限於血壓的測量;在連續方法上,傳統採開迴路控制,並未能考慮感測器和動脈血管間的耦合性,可是動脈血管為一彈性的軟管,所以它的順應性是一隨壓力改變的非線性函數,因此在長時間記錄時若無考慮感測器和動脈血管間的匹配問題,所測量到的波形準確性就會降低。為改善此缺點,本論文提出以模糊邏輯的方法來增加斷續和連續血壓測量的準確性,並同時可獲得動脈血管的順應性。在壓振式血壓測量上,由於人為的晃動或心血管疾病大大影響壓振波振幅的變化,進而造成血壓測量的不準確,所以採用遞迴式加權迴歸驅進法(RWRA)來降低壓振波振幅的變化。此方法包含有模糊邏輯辨別(FLD)和遞迴式迴歸法,模糊邏輯辨別是用來決定受到晃動或心血管疾病對壓振波影響的可信度,依照此可信度配合遞迴式迴歸法,可重建臂帶壓和壓振波振幅的關係,而動脈血管的順應性亦包含於其中,因此不但能增加血壓測量的穩健性,並能獲得一個新的動脈血管順應性指標和動態特性。在連續血壓測量上,設計一個彈性薄膜的動脈壓張計,用於記錄連續血壓波和血管體積波,為了達到精確的測量,動脈壓張計的腔壓必須維持於平均動脈血壓,稱為最佳化匹配條件,因為在此狀態下動脈有最大的順應性。又因為平均動脈血壓不能直接測量,所以設計一個以血管模型基礎的模糊邏輯控制器,利用自製的微量注射裝置,對動脈壓張計的腔壓做補償,使腔壓能跟隨動脈平均壓的變化。此控制系統包含有線性預估器、Kalman濾波器和一個合成型的模糊控制器(SFLC)。線性預估器是由動脈壓力和體積的模型所建立,用於每次心跳即對合成型的模糊控制器的功能做調整;Kalman濾波器是去降低動脈壓張計所記錄的血管體積波振幅受到生理或測量時的擾動量;合成型的模糊控制器是由三個平行的子控制器所組成,每一個子控制器為簡單型的模糊邏輯控制器,分別處理平均動脈血壓的三種狀態:穩定狀態、上升狀態和下降狀態。控制器內的模糊法則採壓振式原理,即為當動脈血管體積變化最大時,血管有最大順應性。所建構的壓振式血壓測量和連續性血壓測量,皆有做臨床驗證或生理訊號的模擬,並與其它的測量和控制方法做比較,本文所提的測量系統是優於目前已存在的血壓
This thesis aims to improve the accuracy in noninvasive oscillometric and continuous blood pressure measurements, and estimate an arterial vessel’s compliance. Capabilities and performances of the proposed scheme have been verified and evaluated with other methods by various examples. Physiological simulation and clinical measurement results show that the proposed measurement systems with associated algorithms and controllers are superior to other measurement systems.
In noninvasive blood pressure measurement, there are intermittent and continuous methods. For the intermittent method, if the patients have cardiovascular diseases, the oscillometry is not more robust than the auscultation. Moreover, the traditional oscillometry only measures the systolic, diastolic and mean arterial pressures. For the continuous method, the traditional measurement system utilized the open loop controller to register the blood pressure waveform. Because the arterial vessel is similar to an elastic tube, its compliance is a nonlinear pressure function. If the coupling condition between the sensor and arterial vessel doesn’t be considered, the measurement system will not detect the accurate blood pressure waveform in long term measurement. In order to improve these defects, this thesis proposed a fuzzy logic methodology to increase the accuracy of intermittent and continuous measurements and furthermore detect the vessel compliance. In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure measurement. A recursive weighted regression approach (RWRA) is used to reduce interference in the OAs. This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. The FLD is used to determine the truthfulness of the oscillation pulse that might be produced by the patient’s motion or cardiovascular diseases. According to the truth degree, the relationship between the cuff pressure and OA is reconstructed using the recursive regression approach. Moreover, the relationship between the cuff pressure and OA contains many vascular characteristics such as compliance. Therefore, this method not only increases the robustness for blood pressure measurement, but also detects a new compliance index and dynamic characteristics of arterial vessel. For a continuous blood pressure measurement, a modified flexible diaphragm tonometer is used to detect the continuous blood pressure waveform and vessel volume pulse. To reach accurate measurement without distortion, the tonometer’s mean chamber pressure must be kept equal to the mean arterial pressure, the so-called optimal coupling condition, such that the arterial vessel has the maximum compliance. Since the mean arterial pressure cannot be measured directly, a model-based fuzzy logic control system is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through a micro syringe device. The proposed control system consists of a linear predictor, a Kalman filter, and a synthetic fuzzy logic controller (SFLC). The linear predictor is to estimate the MAP''s changing tendency based on the identified arterial pressure-volume model and then to beat-to-beat adjust the function of SFLC. The Kalman filter is to reduce the physiologic and measurement disturbance of the vessel volume oscillation amplitude. The SFLC is composed of three parallel subcontrollers, each of which is a simple fuzzy logic controller, for processing the three changing states of the MAP: ascending, descending, and stabilizing states, respectively. The design of the fuzzy rules in each subcontroller is based on the oscillometric principle saying that the arterial vessel has the maximum compliance when the detected vessel volume pulse reaches its maximum amplitude. The constructed blood pressure measurement systems including oscillometry and continuity are verified by clinical experiments and physiologic examples with/ without noise conditions through computer simulations. The results have demonstrated the advantages of the proposed approaches over the existing ones.
Contents
Abstract in Chinese‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧i
Abstract in English‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧iii
Contens‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧vi
List of Figures‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧ix
List of Tables‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧xiv
List of Nomenclatures‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧xv
1 Introduction‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧1
1.1 Historical Review‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧1
1.2 Research Objectives and Organization of Thesis‧‧‧‧‧‧‧‧‧‧‧5
2 Reduction of Interference in Oscillometric Arterial Blood Pressure and Compliance Measurement Using Fuzzy Logic‧‧‧‧‧‧‧‧‧‧‧‧‧6
2.1 Introduction‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧7
2.2 Fuzzy-Logic-Based Recursive Weighted Regression Approach‧‧‧‧‧11
2.2.1 Fuzzy Logic Discriminator for Disturbance Estimation‧‧‧‧‧‧13
2.2.2 Recursive Weighted Regression Approach ‧‧‧‧‧‧‧‧‧‧16
2.2.3 Pressure-Volume Relationship and Compliance Calculation‧‧‧‧18
2.2.4 Arterial Pressure-Volume Curve‧‧‧‧‧‧‧‧‧‧‧‧‧‧20
2.3 Experimental Results‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧22
2.3.1 Experimental procedure‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧22
2.3.2 Statistical Analysis‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧24
2.3.3 Arterial Pressure-Volume Curve‧‧‧‧‧‧‧‧‧‧‧‧‧‧29
2.4 Discussions and Conclusions‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧31
3 A Modified Tonometer Device and the Function Analysis‧‧‧‧‧‧‧‧36
3.1 Introduction‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧37
3.2 Sensory and Chamber Pressure-Volume Models‧‧‧‧‧‧‧‧‧‧‧41
3.2.1 Structure and Principle of the Tonometer‧‧‧‧‧‧‧‧‧‧‧41
3.2.2 Electronic Design and Calibration‧‧‧‧‧‧‧‧‧‧‧‧‧43
3.2.3 Mathematical Oscillometric Model of the Tonometer‧‧‧‧‧‧46
3.2.4 Model of Chamber Pressure-Volume Relationship‧‧‧‧‧‧‧52
3.2.4.1 Nonlinear Model‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧53
3.2.4.2 Dynamic Model‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧54
3.3 Valsalva Maneuver Experiment and Compliance Measurement‧‧‧‧‧57
3.3.1 Measurement System‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧58
3.3.2 Experiment Protocol‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧61
3.3.3 Results‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧61
3.3.3.1 Compliance Measurement‧‧‧‧‧‧‧‧‧‧‧‧‧62
3.3.3.2 Valsalva maneuver‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧64
3.4 Discussions and Conclusions‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧68
4 A Model-based Fuzzy Logic Controller for Tracking Mean Arterial Pressure
‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧73
4.1 Introduction‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧74
4.2 Model-based Synthetic Fuzzy Logic Control‧‧‧‧‧‧‧‧‧‧‧‧78
4.2.1 Kalman Filter‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧79
4.2.2 Model-based Linear Predictor‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧80
4.2.3 Micro Syringe Device‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧81
4.2.4 Synthetic Fuzzy Logic Controller‧‧‧‧‧‧‧‧‧‧‧‧‧82
4.3 Experiment and Simulation Results‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧87
4.3.1 Experimental Process‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧87
4.3.2 Performance of SFLC Comparisons‧‧‧‧‧‧‧‧‧‧‧‧88
4.3.3 The Real condition of Simulation Results‧‧‧‧‧‧‧‧‧‧95
4.4. Conclusions‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧100
5 Conclusions‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧‧102
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