血液透析治療過程中靜脈針脫針會引起嚴重失血危及生命，因此需要及時發現並採取補救措施。本論文針對血液透析過程中血液滲漏的偵測進行研究。我們提出了一套額外的臨床工具與配置系統，主要針對透析病患進行即時偵測於血液透析過程中血液滲漏之遠程監控裝置。這種偵測裝置為整合軟質感測器、血液滲漏偵測方法與無線通信且可以在護理站進行遠程監控。此裝置可以改善血液透析過程中的血液滲漏安全機制。在輔助工具中，我們結合了軟質感測測器、智能模型、嵌入式系統與WiFi通信解決了血液滲漏檢測安全問題，並提出了兩種使用不同類型的軟質感測器和血液滲漏偵測方法來實現該配置。首先，設計軟質感測器與虛擬自組織直流網路模型以檢測血液透析治療過程中的血液滲漏。與其他現有的感測器設計相比，提出的軟質感測器可以減少類比電路元件(如電子電路、紅外線光源、光感測器和印刷電路板上的集成電路)的大量需求。所提出的檢測模型已經由256個連接狀態的組合進行了可行性驗證，實驗結果顯示血液滲漏偵測命中率100％。 其次，整合軟質光陣列感測器和機器學習模型以檢測透析期間血液滲漏。此設計與紅外光感測器相比，軟質光陣列感測器體積小、成本低、功率低，易於實現在穿戴式設備中。最後，使用豬血模擬人血進行血液洩漏偵測的實驗，實驗結果顯示100％的命中率和100％的真實陽性率。

Serious blood loss incurred by venous needle dislodgment during hemodialysis therapy is life-threatening; hence timely detection and remedy are required. In this thesis, the detection of blood leakage during hemodialysis was investigated. We propose a configuration which provides an additional clinical tool for timely detection and alert as well as remote monitoring of blood leakage during hemodialysis. This configuration integrates flexible sensors, blood leakage detection methods, wireless communication, and cloud computing which allows remote monitoring of hemodialysis patients at the nurse station and functions as an assistant tool to improve blood leakage detection safety mechanism during hemodialysis. In the assistant tool, we combined flexible sensors, intelligent models, an embedded system and WiFi communication to solve the blood leakage detection safety problem. Two models using different types of flexible sensors and methods for blood leakage detection are proposed to implement the configuration. First, flexible array sensors and a virtual self-organizing DC grid model are designed and constructed to detect the blood leakage during hemodialysis therapy. In contrast to existing two spaced out designs, this alarm sensor design could reduce the large requirements of analog circuit elements, such as electronic circuits, IR light sources, photo detectors, and integrated circuit chips on printed circuit boards. The feasibility of the proposed detection model was verified using 256 combinations of connecting states. The experimental results indicated 100% of hit rate. Second, photocell array sensors and a machine learning model are designed and constructed to detect vascular access hemorrhage during dialysis. In contrast to the IR light sensors, the photocell sensor was small, of low cost, consumed low power, and was easy to implement in a wearable device. Blood leakage/loss experiments were performed using pig blood. The experimental results indicated a hit rate of 100% and a true positive rate of 100%.

Contents
CHAPTER 1 Introduction 1
1.1 Preface 1
1.2 The End-Stage of Renal Disease 3
1.3 Different Types of Dialysis 4
1.4 Literature Review 6
1.5 Motivation of the Thesis 13
1.6 Contributions of This Thesis 14
1.7 Outlines of Contents 16
CHAPTER 2 Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model for Blood Leakage Detection during the Hemodialysis 17
2.1 Introduction 17
2.2 The Overall Architecture of the Proposed Blood Leakage Detection System 17
2.3 Flexible Sensor and Embedded System 19
2.4 Virtual DC Grid Model 25
2.5 Discussion the Equivalent DC Grid and Diffuseness of Blood Leakage 35
CHAPTER 3 Integration of Photocell Sensors and Intelligent Machine Learning Model to Detect Vascular Access Hemorrhage during Dialysis 37
3.1 Introduction 37
3.2 The Overall Architecture of the Proposed System 37
3.3 Flexible Array Photocell Sensors 40
3.4 Machine Learning Model 43
3.5 Control Output Alarm Unit 53
CHAPTER 4 Experimental Results and Discussions 56
4.1 Introduction 56
4.2 The Embedded System Overview 56
4.3 Experimental Results of the Virtual Self-Organizing DC Grid Model 58
4.4 Experimental Results of the Hetero-Associative Memory Model 68
4.5 Performance Comparisons 75
CHAPTER 5 Conclusions and Future Directions 78
5.1 Conclusions 78
5.2 Future Directions 82
Reference 83

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