臺灣博碩士論文加值系統

新生兒加護病房的高危險嬰兒經常遇到血流動力學不穩定的問題，血液循環不順暢可能導致休克或其他後遺症。但休克的症狀在初期不易被察覺，臨床上大多依賴有經驗的醫師判斷。因此，如何有效地評估新生兒的血液循環狀態對於即時治療具有重要意義。雖然有一些儀器可以估計血流量的訊息，如雷射都卜勒灌注成像 (laser Doppler perfusion imaging, LDPI)、雷射都卜勒血流儀 (laser Doppler flow meter, LDF)、側流暗場 (sidestream dark field, SDF) 成像、正交偏振光譜(orthogonal polarization spectroscopy, OPS) 成像和對比劑磁振血管攝影 (contrast-enhanced magnetic resonance angiography, CE-MRA)，但是目前缺乏直接評估新生兒血液循環的監測系統。本研究提出一種基於近紅外光譜技術的智慧型新生兒血液灌注評估系統，可以同時監測血紅蛋白濃度和組織氧飽和度變化並進一步評估新生兒的血液灌注。通過施加壓力和放鬆壓力下血紅蛋白參數的變化定義幾個指標，以獲得新生兒灌注訊息。此外，基於神經網路的分類器可以有效地對不同血液灌注狀態的組別進行分類。從實驗結果來看，不同血液灌注狀態組別之間的差異可以準確地反映在一些定義的指標上，並且可以利用神經網路技術進行有效識別。

High-risk infants in the neonatal intensive care unit often encounter the problems with hemodynamic instability, and the poor blood circulation may cause shock or other sequelae. But the appearance of shock is not easy to be noticed in the initial stage, and most of the clinical judgments are subjectively dependent on the experienced physicians. Therefore, how to effectively evaluate the neonatal blood circulation state is important for the treatment in time. Although some instruments, such as laser Doppler perfusion imaging (LDPI), laser Doppler flow meter (LDF), sidestream dark field (SDF) imaging, orthogonal polarization spectroscopy (OPS) imaging, and contrast-enhanced magnetic resonance angiography (CE-MRA), can estimate the information of blood flow, there is still lack of monitoring systems to evaluate the neonatal blood circulation directly. Based on the technique of near-infrared spectroscopy, an intelligent neonatal blood perfusion assessment system was proposed in this study, to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion. Here, several indexes were defined from the changes of hemoglobin parameters under applying and relaxing pressure to obtain the neonatal perfusion information. Moreover, the neural network-based classifier was also used to effectively classify the groups with different blood perfusion states. From the experimental results, the difference between the groups with different blood perfusion states could exactly be reflected on several defined indexes and could be effectively recognized by using the technique of neural network.

摘 要----i
Abstract---ii
誌 謝---iv
Contents----v
List of Figures-vi
List of Tables--vii
Chapter 1 Introduction---1
1.1 Background--1
1.2 Previous research--2
1.3 Motivation--3
1.4 Organization structure of thesis--4
Chapter 2 Methods---5
2.1 Basic principle of near-infrared spectroscopy---5
2.2 Design of smart blood perfusion monitoring system---7
2.3 Experimental design---12
2.4 Neonatal blood perfusion classification---16
Chapter 3 Results---18
3.1 Neonatal blood perfusion indexes corresponding to different groups-----18
3.2 Performance evaluation of blood perfusion assessment system----22
Chapter 4 Discussions---26
Chapter 5 Conclusions---30
References------31

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