麻醉監測技術進步與優化病人安全_應用小波頻譜分析麻醉動態自主神經反應

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姓名

王馨苡(Hsin-Yi Wang) 查詢紙本館藏

畢業系所

生醫科學與工程學系

論文名稱

麻醉監測技術進步與優化病人安全_應用小波頻譜分析麻醉動態自主神經反應
(Advancements in Anesthesia Techniques and Monitoring Technologies:Optimizing Patient Care and Safety)

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摘要(中)

本研究探討了Propofol誘導過程中的自主神經系統反應及其與鴉片類藥物的藥效協同作用，使用小波頻譜分析（wavelet-based spectral analysis）進行高時間解析率分析。結果顯示，在Propofol bolus誘導的早期階段，副交感神經活動顯著被抑制，隨後交感神經才開始抑制，這表明Propofol對自主神經系統的不同層面的影響具有明顯的時序性特徵，這些發現與臨床觀察到的生命徵象一致。小波分析技術使我們能夠準確擷取到這些快速且複雜的變化，突顯了其在理解麻醉誘導過程中動態自主神經反應的重要性。
此外，還應用了基於機器學習的反應曲面模型來分析Propofol和鴉片類藥物對自主神經功能的協同藥效。高解析度的小波分析為反應曲面模型提供了詳細的數據，使我們能夠精準地分析這些藥物對自主神經系統的協同作用。結果顯示，Propofol與鴉片類藥物對自主神經功能抑制存在顯著的協同效應。這種協同作用的熱力學解釋表明，Propofol和鴉片類藥物的協同作用會提高麻醉深度，兩種藥物同時使用需要減少單一藥物的使用量，進而降低單一種高劑量藥物副作用風險，增強麻醉的安全性和有效性。
為尋求安全麻醉誘導方式，我們進一步比較不同給藥方式造成的自主神經影響。比較標靶控制輸注與傳統一次手動推注給藥，發現標靶控制輸注在麻醉早期誘導期間提供更穩定的自主神經反應。相反，一次性地給予誘導劑量會導致較大的自主神經波動。本研究更進一步研究Propofol與鴉片類藥物合併使用對自主神經的反應曲面模型。應用了小波分析技術在準確擷取自主神經反應的高時間解析度在麻醉上的優勢，為臨床麻醉劑量的優化提供新的見解，有助於提高麻醉的安全性和效果。未來的研究應進一步探討Propofol與鴉片類藥物的最佳劑量組合用法，以實現更加個體化和精確的麻醉臨床處置。

摘要(英)

This study investigates the autonomic nervous system response during propofol induction and its pharmacodynamic synergy with opioids, combined with wavelet-based spectral analysis for high-resolution temporal assessment. Results demonstrate significant early-phase parasympathetic inhibition followed by sympathetic withdrawal during propofol bolus induction, indicating distinct temporal characteristics of propofol′s impact on the autonomic nervous system, consistent with clinical observations. Wavelet analysis accurately captures these rapid and complex changes, highlighting its importance in understanding dynamic autonomic responses during anesthesia induction.
Additionally, a machine-learning-based response surface model was utilized to analyze the synergistic pharmacodynamic effects of propofol and opioids on ANS function. High-resolution wavelet-based spectral analysis provided detailed data, enabling precise determination of drug interactions on the ANS. The findings reveal significant synergistic effects between propofol and opioids, enhancing anesthesia depth while reducing individual drug dosage. Thermodynamic analysis supports that this synergy improves anesthesia safety and efficacy by minimizing side effects.
Comparing target-controlled infusion and traditional manual bolus, target-controlled infusion showed fewer heart rate variability changes, providing more stable cardiovascular responses during early induction. In contrast, MB led to greater autonomic fluctuations, increased sympatho-vagal activity, decreased parasympathetic activity, and reduced peripheral sympathetic activity.
This study not only explores dynamic changes in ANS responses during propofol induction but also provides a machine-learning-based response surface model for the synergistic effects of propofol and opioids on ANS function. wavelet-based spectral analysis′s ability to capture high-resolution temporal patterns of autonomic responses offers critical insights into optimizing anesthesia dosing, enhancing anesthesia safety, and efficacy in clinical practice. Future research should further investigate optimal dosage combinations of propofol and opioids for personalized anesthesia management and explore alternative induction methods or drug combinations to broaden clinical applicability.

關鍵字(中)

★ 麻醉
★ 小波頻譜分析
★ 自主神經活性
★ 光體積變化描記圖法
★ 心律變異

關鍵字(英)

★ propofol anesthesia
★ autonomic nervous system
★ heart rate variability
★ pulse photoplethysmography
★ wavelet-based spectral analysis

論文目次

Table of contents
中文摘要 i
Abstract ii
Acknowledgements iv
Table of contents vi
圖目錄 ix
表目錄 xi
Chapter 1 Introduction and motives 1
Chapter 2 Literature search and review 4
2.1 Autonomic nervous system 4
2.1.1 Heart rate variability 5
2.1.2 Photoplethysmography 10
2.1.3Clinical Monitoring Systems for Autonomic Nervous System Activity 14
2.2 Anesthetic drugs 18
2.2.1Propofol 18
2.2.2 Opioids 23
2.3 Intravenous Drug Delivery Systems 25
2.3.1 Target Controlled Infusion (TCI) 27
2.4 Response Surface Model 29
2.4.1 Greco Model (two drug) 31
2.4.2 Minto Model (two drug and three drug) 32
2.4.3 Machine Learning-Based Response Surface Model 34
Chapter 3 Identifying Clinical Challenges 36
3.1. Autonomic Nervous System Response to Propofol Induction 36
3.1.1 Hemodynamic Stability 36
3.1.2 Conflicting Evidence 37
3.1.3 Measurement Methods 37
3.2 Ability of WBSA vs. FFT in Capturing Anesthetic Dynamics 37
3.2.1 Improved Time Resolution 37
3.2.2 Direct Measurement 37
3.2.3 Granular Analysis 37
3.3 Identifying Stable Anesthetic Induction Methods with WBSA Analysis 38
3.3.1 Target Controlled Infusion (TCI) 38
3.4 Utilizing Machine Learning-Based Response Surface Models (MLRSM) 38
3.4.1 Synergistic Effects of Anesthetic Combinations 38
3.4.2 Optimal Anesthesia Depth 39
Chapter 4 Methods 40
4.1 Subjects and Preparation 40
4.1.1 Propofol bolus only 40
4.1.2 Profol TCI infusion 41
4.1.3 Propofol bolus with fentanyl 42
4.2 Signal Acquisition and Processing 44
4.2.1 ECG data pre-processing 44
4.2.2 Continuous wavelet transform 45
4.2.3 Fourier analysis 46
4.2.4 Pulse photoplethysmography analysis and amplitude extraction 47
4.2.5 Data normalisation and analysis 48
4.3 Pharmacokinetic simulation and model derivation 48
4.3.1 Thermodynamic Interpretation 49
4.3.2 Machine Learning-Based Response Surface Model 51
4.3.3 Multi-drug MLRSM 53
4.3 Statistical analysis 56
4.3.1 Propofol bolus only, and compare with FFT 56
4.3.2 Profol TCI infusion, compare with bolus 57
Chapter 5 Results 59
5.1 Propofol bolus only 59
5.1.1 Demographic data 59
5.1.2 Signal Acquisition and Processing 60
5.1.3 Pharmacokinetic simulation 66
5.2 Propofol TCI infusion 67
5.2.1 Demographic data 67
5.2.2 Signal Acquisition, Processing, and analyses 67
5.2.3 Pharmacokinetic simulation 70
5.3 Thermodynamic Interpretation of the Response Surface Model 71
5.3.1 Demographic data 71
5.3.2 Validation and Visualization of the Single-Drug MLRSM 72
5.3.3 Two-drug MLRSM and What over the Conventional RSMs 74
Chapter 6 Discussion 79
6.1 The ability of WBSA vs. FFT in Capturing Anesthetic Dynamics 79
6.2 Identifying Stable Anesthetic Induction Methods with WBSA Analysis: 89
6.3 Pharmacodynamic Synergy between Propofol and Opioids 92
Chapter 7 Conclusion 96
References 98
Glossary 105

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指導教授

林澂(Chen Lin)

審核日期

2024-7-26

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