2011-08-012024-05-14https://scholars.lib.ntu.edu.tw/handle/123456789/660580摘要：麻醉過程中，可以全程地觀察到病患由清醒進入麻醉狀況爾後又回復至清醒的變化，其牽涉到身體中各生理系統之協調與改變，臨床上是藉由生理訊號，如血壓、心跳、呼吸、腦波等來判斷麻醉深度 (depth of anesthesia， DOA)，然而目前的判斷方法對於麻醉醫師在判斷 DOA 上資訊尚不足夠。一般麻醉狀況下心跳、血壓會受到呼吸頻率和藥物的影響，經過頻譜分析後自主神經的變化會降低，使得結果與真實的自主神經作用產生誤差。而腦波的量測，則是頭皮表層的訊號，無法表達腦部深層自主神經活動。本計劃主要是透過雷射都卜勒血流計 (Laser DopplerFlowmetry)、模組式重症生理監視器（MP60）和雙頻譜腦電波儀(Bispectrum Index,BIS)收集血流、心跳、SpO2和腦波訊號。藉由分析這些訊號的變化，來討論當病人在清醒與麻醉及麻醉恢復期間，其意識、疼痛狀況及自主神經之間變化的相關性。我們將整個開刀過程中的生理訊號收集後，透過快速傅立業轉換 (FFT)、(DetrendedFluctuation Analysis, DFA) 與希伯特黃轉換 (HHT) 等線性及非線性方法分析，研究病患在開刀過程中的交感神經與副交感神經的變化，再與SE (State Entropy)、RE(Response Entropy)、BIS 和手術壓力指標(SSI)做比較，建立人工智慧多維度生理訊號診斷系統，且藉由醫療團隊所提供的專業醫學知識，回饋修正系統的參數，再於臨床實驗進行驗證。進一步希望能透過電腦端確認了以上的分析方式後，整合臨床上所得出的資料，建立完整的基本麻醉深度的資料庫，運用其發展成即時的分析系統。預期可以發展成清醒度、平衡度之量測，進而運用於日常生活中所需的活動中，例如：駕駛者之生理安全監測、在家安全生理監測、遠距安全生理監測等。<br> Abstract: During the processes of anesthesia in surgical operations, the completely transferringstages from conscious to unconscious and back to conscious again can be clearly observed.An anesthesia stage is a dynamic balance status among the physiological systems in ahuman body. In the clinical practices, the depth of anesthesia (DOA) is evaluated by themedical doctor via the physiological signals and vital signs. However, the information forDOA evaluation is not enough to an anesthesiologist. Considering the changes of heartbeat and blood pressure affected by the breathing rate and the anesthetic drugs in generalanesthesia, the techniques of fast Fourier transform (FFT) can be used to evaluate theactivity of automatic nerve system (ANS). However, the evaluation results by FFT areinsufficient to reflect the exact activity of ANS. Moreover, EEG signals cannot reflect theunderlying activities of the brain stem, which drives the activity of ANS. In this proposalof research, Laser Doppler Flowmetry, physiological monitoring system (MP60）and themonitoring equipment of Bispectrum Index (BIS) are integrated to collect thephysiological signals of blood flow, heartbeat time series, SpO2 and EEG. Then, theinnovative analysis algorithms can be used to quantify the characteristic factors of thosecollected physiological signals for evaluating the DOA during anesthesia, the recovery ofconsciousness after surgical operation, and the pain scales on post operative stage. Thoseinnovative analysis algorithms are FFT、Detrended Fluctuation Analysis (DFA), andHilbert Huang transform (HHT). These analysis algorithms will be used to investigate thedynamical characteristics of physiological signals for monitoring the activities of ANS.Thus, an intelligent monitoring system for DOA is considered as an integrated system,which associates the consciousness evaluation using EEG with the monitoring functionsof ANS activity and surgical stress index. Furthermore, the outputs of DOA monitoringwill be compared with the outcomes of spectral entropies and BIS. In summary, thisinvestigation is supposed to integrate the techniques of data collecting and analysis andclinical proof experiments for contributing a DOA monitoring system and a physiologicaldatabase. As the further applications, the techniques and the database developed in thisinvestigation are considered to be applied in monitoring systems for driver, elderly peopleand homecare patents.