腦血流自動調控（cerebral autoregulation, CA）是當動脈血壓發生變化時，使腦血流維持恆定和預防損傷的生理機制。動態腦血流自動調控可以抵消數十秒內發所發生的血壓變化。此腦部活動可以藉由動脈血壓與腦血流之間的時間關係進行評估，使得其適用於臨床上的連續監測。在本項研究中，我們探討了量測動脈血壓與腦血流的新穎非侵入式的替代方案，使得腦血流自動調控的評估可以更廣泛。我們對這些方法在兩個組進行研究，分別為健康之成人志願者以及因神經血管疾病而正接受神經外科手術的兒童。並且對於這些方法的可行性與表現做初步評估。
為量測動態腦血流自動調控，我們首先探討脈波傳遞時間（pulse transit time, PTT）來作為動脈血壓的量測方法。脈波傳遞時間是血液從心臟離開後到達周邊血管的時間，同時也可作為動脈血壓的替代。使用脈波傳遞時間的優點在於，它可以使用傳統病人監護儀中，常規紀錄的心電圖與血管容積變化訊號波形所計算出來。我們計算出兩種品牌之病人監護儀所計算出來的脈波傳遞時間，與專業非侵入式動脈血壓量測設備（Finapres）和測量動脈血壓之標準侵入式壓力感測器，進行比較。對於腦血流的量測，我們使用擴散相關光譜儀（diffuse correlation spectroscopy, DCS）來替代傳統使用經顱都卜勒（transcranial Doppler, TCD）進行腦血流之量測。擴散相關光譜儀通過擴散動態光散射來光學測量腦血流。我們採用擴散相關光譜儀與動脈導管之動脈血壓，來針對神經系統疾病組進行時域與頻域上的動態自動調控的分析。我們更進一步對於不同的動脈血壓量測方法所得到之腦血流自動調控結果進行了比較—由擴散相關光譜儀與脈波傳遞時間所計算之腦血流自動調控、由擴散相關光譜儀與動脈導管之動脈血壓所計算之腦血流自動調控。這些技術被整合以用於評估我們所開發出的新腦血流自動調控方法，我們運用此新方法在兩組不同的情況下：神經系統疾病組與健康受試者組。
首先，在其中一種品牌之病人監護儀中，我們得出了脈波傳遞時間與動脈血壓在時域之間的高相關性（平均值：0.7，標準差：0.15）。而在頻域的比較中，我們也展示了在脈波傳遞時間和動脈血壓都共同受到呼吸器產生的胸腔內壓力震盪影響，而產生相似的調控。在腦血流自動調控的時域與頻域分析中，我們分別採用皮爾森積差相關分析（Pearson correlation）與傳遞函數相關分析（transfer function analysis, TFA）。在時域部分，兩種不同的動脈血壓量測方法展現出了高相關性。
本研究仍存在一些尚待解決的議題。例如，病人監護儀輸出數據存在同步問題，因此現階段無法使用脈波傳遞時間來完美地估計動脈血壓。我們設計了一個基於微控制器的心電圖體積描記記錄板，將應用在我們未來的研究中，以用於瞭解病人監護儀內置後處理程序。此外，手術過程中神經系統疾病組的記錄過程是不可控的。為獲得更可靠的數據來分析腦血流自動調控，開發運動偽影檢測算法是不可或缺的。本研究提供了使用脈波傳遞時間和擴散相關光譜儀代替傳統方法進行腦血流自動調控分析的初步結果，並在不同的病例、神經系統疾病組和健康組中實施我們新的測量方法。

Cerebral Autoregulation (CA) is a mechanism in the brain to stabilize cerebral blood flow (CBF) from fluctuations in arterial blood pressure (ABP) to maintain brain function and prevent injury. Dynamic CA counteracts changes in blood pressure occurring over tens of seconds. Its activity can be assessed from the temporal relationship between ABP and CBF, making it suitable for continuous monitoring in clinical settings. In this research, we investigated use of new, non-invasive alternatives for measuring ABP and CBF to make CA assessment more widely available. We studied their use in two groups, healthy adult volunteers and children undergoing neurosurgery for neurovascular disease, and performed a preliminary evaluation of their feasibility and performance.
To measure dynamic CA, we first studied pulse transit time (PTT) to replace other ABP measurement methods. PTT is the travel time for blood leaving the heart to reach the periphery and is a proxy for ABP. The advantage of PTT is that it can be calculated from routine ECG and plethysmography waveforms available from any conventional patient monitor. We compared PTT from two different brands of commercial patient monitors with ABP from a specialized non-invasive device (Finapres) and gold-standard invasive arterial pressure sensors. For CBF measurement, we used diffuse correlation spectroscopy (DCS) instead of conventional transcranial Doppler (TCD). DCS measures CBF optically by diffuse dynamic light scattering. We analyzed dynamic CA in neurological disorder group by DCS and ABP with time domain analysis and frequency domain analysis. We further compared the CA result from different ABP measuring methods: CA from DCS and PTT, CA from DCS and A-line. These techniques were integrated to evaluate the utility of our new CA measuring method for both neurological disorder and healthy groups.
First, for one patient monitor brand, we found high correlation [mean 0.7 (SD 0.15)] between PTT and gold standard ABP in time domain analysis. In the frequency domain, we show PTT and ABP were both modulated similarly by oscillations in intrathoracic pressure from mechanical ventilation. Pearson correlation and transfer function analysis (TFA) were applied for time domain analysis and frequency domain analysis for CA analysis, respectively. The time domain result showed high correlation between CA with different ABP measuring methods.
There are still some issues that remain in this research. For example, patient monitor output data have potential synchronization problem, thus PTT cannot be used to estimate ABP perfectly. We designed a microcontroller-based ECG- plethysmography recording board to be implemented in our future study as the ground truth to discover the patient monitor in-built post-processing procedures. Besides, the recording of neurological disorder group during the surgery is uncontrollable. To approve the data quality for more reliable CA results, developing a motion artifacts detection algorithm is necessary. This research provided preliminary results of using PTT and DCS instead of conventional methods for CA analysis, and implemented our new measuring methods on different cases, neurological disorder groups and healthy groups.

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