臺灣博碩士論文加值系統

本研究是利用模糊控制理論做手術中的肌肉麻醉控制之相關研究，在臨床實驗部分共進行三種控制：一大劑量控制、密集的手動控制以及應用模糊理論為基礎的自動控制，結果發現模糊控制器與手動控制並無顯著差異，接著本研究利用藥理學建立起一肌肉鬆弛劑Cisatracurium的藥物模型，其中，藥物動力學模型是利用Kisor教授的實驗結果，藥物藥效學模型是利用本研究的臨床實驗結果推得，最後發現在麻醉誘導期和麻醉恢復期與實際情況有些許不同，原因在於本模型並無考慮其他麻醉藥物的交互影響；接著，本研究利用模糊模型理論擷取出麻醉醫師的臨床控制規則，並與其口述的規則比較，透過藥物學模型的模擬得知，萃取出的控制規則較佳；為了能改進此控制器的性能，本研究應用了機率式的模糊模型理論重新擷取控制規則，模擬結果顯示新規則並無優於舊式的規則，推測其原因在於平均誤差並不能代表整體決策的性能表現，若能將每筆控制誤差給予不同權重，控制結果應可優於先前的控制結果。

Despite Cisatracurium’s long-onset time, it is still widely used in regular surgical operations. A complete solution in terms of the hardware, software, and control methodology for controlling the degree of muscle relaxation is presented in this thesis. Our development procedure includes three tasks; the first task was to collect the clinical data using three different control methods – intermittent bolus control, intensive manual control, and automatic control. Intermittent bolus control was used on 13 patients. Intensive manual control was used on 15 patients. Automatic control was used on 15 patients. These control results showed that the mean (SD) of the mean error for each method was 8.76 (1.46), 1.65 (1.67), and 0.48 (1.43), respectively. Although these statistics showed that the automatic control method was not significantly different to the intensive manual control method, it was more accurate.

The second task was to build a patient model for evaluating the performance of the controllers. The method for building a compartmental numerical model, with two compartments based on pharmacokinetics and one compartment based on pharmacodynamics, is presented. The coefficients for the pharmacokinetic model were identified and based on Kisor’s research results. The pharmacokinetic model result showed that our model was 4.6% different to Kisor’s. The pharmacodynamic model result also showed that the two parameters of Hill Equation (50% of the maximum effect and an eliminating constant) were 70.93 ± 36.84 and 1.24 ± 0.29, respectively.

The final task was to extract the control rules from the clinical manual control data and to mimic the anesthesiologist’s behavior via the automatic control method. Completion of the patient model helped us with the final design of the controller. We have described how to extract the control rules using fuzzy modeling method. This thesis also presents two rule-bases: one from the fuzzy modeling method and the other from anesthesiologists’ clinical experience. They were compared with four tests: the different set points, the control interval strategy, the tolerance of noise effect, and the delay time effect.

The simulation showed that the fuzzy modeling algorithm could successfully extract the fuzzy rules from the clinical data, and its control error was smaller than the anesthesiologist’s rules for different set point tests. However, the control error increased and became worse when the set points were raised. It meant that these two rule-bases were not apt to control the higher set points (i.e. T1% of 40 or higher). The t-test also showed that these two rule-bases performance at different set points had significant differences (p < 0.05). Moreover, the results for the control interval tests showed that strategy had significant influence, especially in reducing standard deviation of control errors. However, these two rule-bases were not affected by noise disturbance, and the delay time only affected the overshoot for these two rule-bases in simulations.

Furthermore, for improving the performance of the extracted rule-base controller, we used the probability-type fuzzy rules extraction method, which each decision point having different weighting factor based on its control performance, and obtained a new rule-base. The simulation results show that it was not better than the previous controller. The reason is the mean of the control error for each anesthesiologist can not represent their overall performance. If the weighting factor could decided by the control result of each decision point, the performance may be better than it decided by the mean of the control error of each patient.

In this study, we not only established a muscle relaxation control system but also provided the control strategies for the drug Cisatracurium. These control strategies were obtained by the anesthesiologists, fuzzy modeling method, and probability-type fuzzy modeling method. Although the performance of the probability-type fuzzy modeling method is not significantly different with the self-organizing fuzzy modeling method, both these two fuzzy modeling methods can obtain the linguistic rules which is directly able to assist the user in realizing the features or related information of the analyzed data.

中文摘要 i
Abstract ii
Tables viii
Figures ix
Abbreviation xi
Symbols xiii

1 Introduction 1
1.1 Anesthesia 1
1.2 Depth of anesthesia 3
1.3 Depth of muscle relaxation 6
1.4 Cisatracurium 9
1.5 Neuromuscular transmission 10
1.6 Muscle relaxants and their mechanism of action 12
1.7 Purpose of this research 13
1.8 Thesis Overview 14

2 Fuzzy Logic Theory 16
2.1 Introduction 16
2.2 Fuzzy Logic Control System 17
2.2.1 Fuzzification 17
2.2.2 Rule Base 18
2.2.3 Fuzzy Inference Engine 20
2.2.4 Defuzzification 22
2.3 Fuzzy Model 23
2.4 Mamdani-type fuzzy modeling 24
2.5 Chapter Summary 25

3 Pharmacokinetic and Pharmacodynamic Model 26
3.1 Pharmacokinetic Model 26
3.1.1 Volume of distribution (Vd) 26
3.1.2 Clearance (Cl) and Maintenance Dose 27
3.1.3 Elimination Rate Constant (k) and Concentration-Time Profile 27
3.1.4 Elimination Half-life (t1/2β) 28
3.1.5 Two Compartment Pharmacokinetic Model 29
3.1.6 Variability in Drug Dosage Requirements 32
3.2 Pharmacodynamic Model 33
3.3 Chapter Summary 35

4 Clinical Experiment Preparation 36
4.1 Introduction 36
4.2 Patients 37
4.3 Anesthesia procedure and dosage regimen 37
4.4 Control System and Data Collection Design 38
4.4.1 Hardware 38
4.4.2 Software 41
4.5 Chapter Summary 43

5 Clinical Control of Neuromuscular Blocker by Cisatracurium During Surgery 44
5.1 Introduction 44
5.2 Clinical Control Methods 45
5.2.1 Intermittent Bolus Control 45
5.2.2 Intensive Manual Control 46
5.2.3 Automatic Control based on Fuzzy Theory 46
5.3 Clinical Control Results 50
5.4 Discussion and Preliminary Conclusion 52

6 Cisatracurium Model Construction 54
6.1 Introduction 54
6.2 Pharmacokinetics and Pharmacodynamics of Cisatracurium 54
6.3 Assessment of the patient model 58
6.4 Simulation Result and Discussion 58
6.5 Chapter Summary 61

7 Control Rule Extraction (I) 62
7.1 Introduction 62
7.2 Fuzzy rule extraction based on fuzzy modeling approach 63
7.3 Fuzzy logic control and the simulation model 70
7.4 Simulation results 71
7.5 Discussion and Conclusions 78
7.6 Chapter summary 80

8 Control Rule Extraction (II) 81
8.1 Introduction 81
8.2 Probability-Type Fuzzy Rule Extraction 81
8.3 Simulation Results 86
8.4 Discussion and Conclusion 88
8.5 Chapter Summary 89

9 Conclusions and Future Works 91
9.1 Conclusions 91
9.2 Future works 93

References 95

Appendix A 100
Appendix B 102
Appendix C 110
Appendix D 113

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