臺灣博碩士論文加值系統

麻醉自動化近年來越來越受到外國的重視，這門學問並不是單單由麻醉醫師或是控制工程師所能夠獨立做到的，這是一項跨領域的技術整合應用，其中最大的難處在於麻醉深度的判別，這也是許多研究學者想在麻醉醫學方面有所突破的重要方向。然而，開刀房中麻醉深度的判別，不單單只是為了知道病人目前現在的麻醉狀況，更是為了進一步來控制病人所需的麻醉藥量，使得病人能在最安全的狀態下完成手術。
本論文便是根據現有的環境和設備，配合台大醫學院麻醉科的麻醉醫師，來完成手術中病人之監視及麻醉深度自動控制的智慧型系統。而首要的工作就是利用智慧型之類神經網路理論、模糊理論、基因演算法及群集分析來建立靜脈麻醉藥Propofol對於人體生理信號的模型，而本論文所篩選的輸入訊號有體重（Weight）和累加過後的Propofol靜脈注射藥量流率（Sum_PIR）等；輸出訊號有血壓（SAP）、心跳（HR）、量測雙頻譜分析指標Bispectral Index（BIS）等，將病人麻醉模型建構好後，再來則是進行模擬控制，確認所採用之比例-積分-微分控制器(Proportional-Integral-Derivative, PID)及Fuzzy控制器能夠穩當的進行麻醉控制。最後再進行本論文的重點臨床試驗，並已完成五個成功案例。

Anesthesia is a taxing discipline for application of automated on-line drug infusion. To keep the safety of the patient during surgical operations, it is important to on-line monitor the anesthetic condition of the patient. However, Depth of anesthesia (i.e. unconsciousness) is harder to define and not readily measurable during surgery. In practice, anesthetists have a number of clinical signs and on-line measurements which can be used selectively for the determination of the patient's state. Therefore, many methods have been used for monitoring of anesthetic depth based on different clinical measurements. In order to simulate the whole operation during intravenous anesthesia, two inputs (i.e., weight and summation of propofol infusion rate) and three outputs (i.e., blood pressure, heart rate and bispectral index signals) fuzzy model with genetic algorithms and clustering have been designed for the patient model. Also, the patient model has been validated and simulated by proportional -integral-derivative (PID) controller and fuzzy logic controller. Furthermore, clinical trials have been tested in five cases during intravenous anesthesia.

封面 i
書名頁 ii
授權書 iii
國科會授權書 iv
博碩士論文電子檔案上網授權書 v
論文口試委員會審定書 vi
中文摘要 vii
英文摘要 viii
誌謝 ix
目錄 x
表目錄 xiii
圖目錄 xiv
縮寫符號說明 xv
第一章、緒論 1
1.1 研究背景 1
1.2 文獻回顧 2
1.3 研究目的 2
1.4 章節簡介 3
1.5 結論 4
第二章、臨床麻醉與生理訊號監測 5
2.1 臨床麻醉的方法 5
2.2 病患開刀麻醉的過程 5
2.3 病人生理訊號的監視 8
2.4 醫師經驗與病人生理訊號的篩選 9
2.5 結論 10
第三章、智慧型理論 11
3.1 類神經網路理論 11
3.2 類神經網路理論應用 12
3.2.1 病人麻醉模型的建立 12
3.3 集群分析 13
3.4 基因演算法 15
3.5 模糊理論 15
3.6 模糊理論應用 16
3.6.1病人麻醉模型的建立 16
3.6.2 模糊控制器 17
3.7 結論 18
第四章、臨床上的資料收集 19
4.1 資料收集的對象 19
4.2 量測儀器及資料收集 19
4.3 資料收集注意事項 21
4.3 結論 22
第五章、病人麻醉模型之建立 23
5.1 以類神經網路為架構之病人模型 23
5.1.1 以不同的輸入建構病人模型 24
5.1.2 初步的結果與討論 25
5.2 以模糊理論為架構之病人模型 28
5.3 以高注射流率的麻醉藥劑作病人麻醉模型測試 31
5.4 結論 32
第六章、病人麻醉深度的模擬控制 33
6.1 傳統PID控制器的數位化控制 33
6.2 以模糊理論實現麻醉模擬控制 38
6.3 加入高斯亂數進行模擬控制 39
6.4 結論 43
第七章、臨床試驗 44
7.1 試驗方法 44
7.2 試驗結果 44
7.3 結論 46
第八章、總論 47
8.1 結論 47
8.2 未來展望 48
參考文獻 49
附錄A：臨床試驗許可書 52
附錄B：PID模擬控制結果 54
附錄C：Fuzzy模擬控制結果 66
附錄D：PID模擬控制結果（含干擾） 78
附錄E：Fuzzy模擬控制結果（含干擾） 90
附錄F：臨床試驗控制結果 102
附錄G：病患基本資料一覽表 107

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