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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾雪峰 | zh_TW |
| dc.contributor.advisor | Snow H. Tseng | en |
| dc.contributor.author | 周昱廷 | zh_TW |
| dc.contributor.author | Yu-Ting Chou | en |
| dc.date.accessioned | 2024-09-15T16:08:46Z | - |
| dc.date.available | 2024-09-16 | - |
| dc.date.copyright | 2024-09-14 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-12 | - |
| dc.identifier.citation | 1. Padgilwar, S., Recent Drug Advances in the Management of Diabetes Complications: An Overview. Tuijin Jishu/Journal of Propulsion Technology. 44(4): p. 2023.
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Diabetes technology & therapeutics, 2005. 7(5): p. 776-779. 57. Sengupta, S., et al., Clarke error grid analysis for performance evaluation of glucometers in a tertiary care referral hospital. Indian Journal of Clinical Biochemistry, 2022: p. 1-7. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95621 | - |
| dc.description.abstract | 本研究利用光體積變化描記（PPG）訊號與深度學習技術，開發非侵入式血糖監測系統。首先使用蒙地卡羅方法（Monte Carlo Method）模擬光子在皮膚內的傳播路徑，以確定光源（LED）與光偵測器（PD）之間的最佳距離。接著，我們開發了血糖量測原型機，利用Arduino結合心率感測晶片(MIKROE 2036)捕捉三種波長(950 nm, 660 nm, 525 nm)的PPG訊號，並進行數據預處理。在深度學習部分，我們使用多層感知器(MLP)、卷積神經網絡（CNN）、殘差神經網絡（ResNet）模型，基於50位受試者提供的250筆三分鐘訊號數據建立血糖預測模型。此外，該系統設計了專為雲端運算而優化的軟體架構，使血糖監控不僅限於單一設備，能夠在任何支援的設備上實時且連續的監控血糖水平。這種便捷和安全的血糖管理方案為糖尿病患者提供了更加有效的健康管理工具。 | zh_TW |
| dc.description.abstract | This study develops a non-invasive blood glucose monitoring system using photoplethysmography (PPG) signals and deep learning technology. Initially, the Monte Carlo Method was used to simulate the photon propagation paths in the skin to determine the optimal distance between the light source (LED) and the photodetector (PD). Subsequently, we developed a blood glucose measurement prototype, utilizing Arduino combined with the heart rate sensing chip (MIKROE 2036) to capture PPG signals at three wavelengths (950 nm, 660 nm, 525 nm) and perform data preprocessing. In the deep learning phase, we employed Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Residual Neural Network (ResNet) models to build blood glucose prediction models based on 250 three-minute signal data samples from 50 subjects. Additionally, the system was designed with a cloud computing-optimized software architecture, enabling real-time and continuous blood glucose monitoring on any supported device, beyond a single device. This convenient and secure blood glucose management solution provides diabetes patients with a more effective health management tool. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-09-15T16:08:46Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-09-15T16:08:46Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Overview of Diabetes 1 1.2 Current Invasive Methods for Diabetes Monitoring 2 1.3 Our Non-Invasive Diabetes Monitoring Approach 3 Chapter 2 Literature Review 5 2.1 Introduction to PPG Technology 5 2.1.1 Principles of PPG Technology 5 2.1.2 Physiological Characteristics Affecting the PPG Waveform 6 2.1.3 Blood Oxygen Measurement Applications of PPG 7 2.1.4 Current Challenges 9 2.2 Application of Deep Learning in Glucose Estimation 11 2.3 Experimental Procedures 12 Chapter 3 Optimizing PPG Signal Quality with Monte Carlo Method 14 3.1 Monte Carlo Method 14 3.1.1 Introduction 14 3.1.2 Principle 15 3.2 Model 21 3.2.1 Skin Model 21 3.2.2 Light Source and Detector 24 3.3 Methodology 25 3.3.1 Heartbeat Cycles 25 3.3.2 Photon Number 27 3.3.3 Impact of BMI on Skin Model 28 3.3.4 Operating Environment 29 3.4 Simulation Results and Discussion 30 3.4.1 PPG Simulation 30 3.4.2 Optimal Distance Between Light Source and Detector for Different BMIs 31 Chapter 4 System Architecture and Data Collection 35 4.1 Hardware Design 35 4.1.1 Microcontroller Unit 36 4.1.2 Sensor 37 4.1.3 Circuit Wiring and Connections 39 4.1.4 Enclosure 40 4.2 Software Design 41 4.3 Recruitment and Data Collection Procedure for PPG Signals 43 4.3.1 Composition of Subjects 43 4.3.2 Experiment process 44 4.4 Data Preprocessing 45 4.4.1 Noise Removal 46 4.4.2 Data Cleaning 56 4.4.3 Signal Transformation 59 4.4.4 Signal Resampling 60 4.4.5 Data Preprocessing Method 62 4.5 Deep Learning 63 4.5.1 MLP (Multilayer Perceptron) 64 4.5.2 CNN (Convolution Neural Network) 66 4.5.3 ResNet (Residual Network) 70 4.5.4 Assessing Deep Learning Models' Accuracy with EGA 72 4.5.5 Assessing Deep Learning Models' Interpretability with Different Data Segmentation Strategies 74 4.6 Cloud Deployment of Models 75 Chapter 5 Result and Discussion 77 5.1 Data Collection 77 5.2 Deep Learning 78 5.2.1 Random Data Split 78 5.2.2 Glucose-Level-Based Split 92 5.2.3 Subject-Based Split 94 5.2.4 Model Performance 96 5.3 Cloud Deployment of Models 97 Chapter 6 Conclusion 100 6.1 Comprehensive Evaluation for PPG-Based Blood Glucose Estimation 100 6.2 Current Capabilities and Future Work 101 REFERENCE 104 | - |
| dc.language.iso | en | - |
| dc.subject | 蒙地卡羅法 | zh_TW |
| dc.subject | 連續監測 | zh_TW |
| dc.subject | 雲端運算 | zh_TW |
| dc.subject | 血糖量測 | zh_TW |
| dc.subject | 光體積變化描記圖 | zh_TW |
| dc.subject | 光學模擬 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Cloud Computing | en |
| dc.subject | Photoplethysmography | en |
| dc.subject | Monte Carlo Method | en |
| dc.subject | Optical Simulation | en |
| dc.subject | Deep Learning | en |
| dc.subject | Blood Glucose Measurement | en |
| dc.subject | Continuous Glucose Measurement | en |
| dc.title | 利用光體積變化描記訊號與深度學習技術於非侵入式雲端血糖連續監測系統 | zh_TW |
| dc.title | Continuous Non-Invasive Glucose Monitoring System Using PPG Signals and Deep Learning on the Cloud | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 蘇國棟 | zh_TW |
| dc.contributor.coadvisor | Guo-Dung Su | en |
| dc.contributor.oralexamcommittee | 于天立 | zh_TW |
| dc.contributor.oralexamcommittee | Tian-Li Yu | en |
| dc.subject.keyword | 光體積變化描記圖,蒙地卡羅法,光學模擬,深度學習,血糖量測,連續監測,雲端運算, | zh_TW |
| dc.subject.keyword | Photoplethysmography,Monte Carlo Method,Optical Simulation,Deep Learning,Blood Glucose Measurement,Continuous Glucose Measurement,Cloud Computing, | en |
| dc.relation.page | 107 | - |
| dc.identifier.doi | 10.6342/NTU202403864 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 光電工程學研究所 | - |
| dc.date.embargo-lift | 2029-08-07 | - |
| 顯示於系所單位： | 光電工程學研究所 | |
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