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研究生:朱大維
研究生(外文):Ta-Wei Chu
論文名稱:自動化分析連續監測電子胎兒心音
論文名稱(外文):Automated Data Analysis for Ubiquitous and Continuous Electronic Fetal Monitoring
指導教授:蘇傳軍蘇傳軍引用關係
指導教授(外文):Chuan-Jun Su
口試委員:蔡篤銘梁韵嘉江行全范書愷
口試委員(外文):Du-Ming TsaiYun-Chia LiangBernard C. JiangShu-Kai Fan
口試日期:2016-06-15
學位類別:博士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:136
中文關鍵詞:電子胎兒監測電腦輔助數據分析行動平台技術自動化電腦診斷
外文關鍵詞:Electronic Fetal Monitoring (EFM)computer-assisted numerical analysisautomated diagnosisrestricted Boltzmann machine
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胎心音監視器是婦產科用來偵測胎兒在母體子宮中狀況的工具,醫生藉由胎心率電子監護等技術解決二個問題: (1)作為一種篩選試驗 (2)發現重度窒息的胎兒,早期發現胎兒宮內窘迫。婦產科醫生必須時時觀察胎心音監視器輸出的數據並做出適當的處理。對於醫師而言時時刻刻待在儀器旁是幾乎不可能達到的事情,因此希望能夠透過病例的探討找出相符合之程式,以幫助醫生們在胎兒狀況的判斷更為清晰,減少誤判或疏忽的情況產生。由於電腦輔助診斷需要即時運算來自於胎兒心跳及母親子宮收縮的大量資料,透過數據的前置處理、電腦運算平台、演算分析、機器深度學習及多平台的運用,將針對胎心音監視器中常見的徵兆進行判斷,預期此系統之建立,將可與行動傳輸系統相結合,協助孕婦進行居家監測,並於危險病徵之初期即將相關資訊傳輸給相關醫療院所,進行即時處理與照護,同時也可協助婦產科醫生在使用胎心監視器判斷胎兒生長或健康狀況時,能夠做出更精確的判斷與準確的抉擇,也將母體與胎兒的傷害降至最小。
Over the past few decades, electronic fetal monitoring (EFM) systems have emerged as a very promising tool to facilitate monitoring by various healthcare practitioners, including midwives, obstetricians, and labor and delivery nursing staff. The inception of electronic fetal monitoring systems encourages the integration of many clinical activities in fetal monitoring; however, current technologies lack a means for automatic and continuous monitoring, thus depriving obstetricians the opportunity to conduct real-time, longitudinal fetal monitoring services. Promoting the use of ubiquitous fetal monitoring services with real time status assessments requires a robust information platform equipped with an automatic diagnosis engine. Therefore, we describe an automated EFM data analyzer (AEDA) capable of continuously and automatically analyzing EFM data. We present the design and development of a real-time EFM diagnosis service based on a restricted Boltzmann machine. This service preprocesses raw heart rate (HR) and uterus contraction (UC) numeric series into discrete peaks and valleys (events) with the assumption that these numeric series are continuous. When abnormal events are detected, the deep restricted Boltzmann machine determines whether a C-section surgical procedure is urgently required. In addition to producing the final determination, this service graphs the raw HR and UC series with marked supporting abnormal events.
摘要 i
ABSTRACT ii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER 1 INTRODUCTION 1
1.1. Motivation 1
1.2. Objective 2
1.3. Method 2
1.4. Thesis Organization 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 Background on fetal monitoring 4
2.1.1 The way of fetal monitoring 5
2.1.2 EFM physiology 5
2.1.3 EQUIPMENT 6
2.1.4 EFM interpretation 7
2.2 COMPUTER APPLICATIONS IN OBSTETRICS 15
2.2.1 Central and Remote Display Functions 15
2.2.2 Computer Analysis of FHR Patterns 15
2.3 Data Processing Tools 16
2.3.1 Character Recognition 16
2.3.2 Template Matching Method 17
2.3.3 Data Smoothing and Curve Fitting 18
2.4 Deep learning 20
2.5 Agents and Multi-Agent systems 21
2.5.1 Multi-agent system applied to health care 23
CHAPTER 3 RESEARCH METHODOLOGY 25
3.1 Research Procedure 25
3.2 Data Processing 26
3.2.1 Data Collection and Pre-processing 27
3.2.2 Computer Vision Aided Fetal Monitoring 28
3.3 Data Interpolation Process (figs. 3.4) 29
3.3.1 Data Smoothing 32
3.3.2 Curve Fitting 33
3.3.3 Obtaining Useful Data Points 34
3.4 Categorization and Analysis of Fetal Heart Monitoring Records 34
3.4.1 Categorization of Symptoms 34
3.4.2 Analysis of Symptoms 35
3.4.3 EFM Pattern Recognition 39
3.4.4 Artificial neural networks 42
3.5 Mobile Multi-Agent Based Open Information System (MAIS) 51
3.5.1 System design 53
3.5.2.IMAIS agent environment 54
3.5.3.IMAIS System Architecture 56
3.5.4.User Interface Agent (UIA) 56
3.5.5.Medical (obstetrician) Agent 57
3.5.6.Resource Agent 57
3.5.7.Diagnostic Agent 58
3.5.8.Knowledge-based Data Server 58
3.5.9.External service 60
CHAPTER 4 RESULTS 61
4.1 Data collection 61
4.1.1 Computer Vision Aided Fetal Monitoring 61
4.1.2 Graph printout converts to digital format 62
4.2 Data processing 64
4.2.1 Key point extraction 65
4.2.2 Statistic grid line add on 66
4.3 Diagnostic guideline and the experimental results 68
4.3.1 Diagnostic logic 71
4.3.2 Test with standard graph 72
4.3.3 Run the diagnostic engine with clinical data (Mathematical Algorithm Method) 75
4.3.4 Run the diagnostic engine with standard data training(neural network with RMB) 76
4.3.5 Run the diagnostic engine with a clinical data (neural network with RMB) 79
4.4 Case Study 81
4.4.1 Case Study using diagnostic engine with mathematical algorithm method 81
4.4.2 Case Study using diagnostic engine with neural network with RBM 83
4.4.3 Case Study: comparison between obstetrician and neural network with RBM 85
4.5 Mobile agent (MAIS) 89
4.5.1 Usage scenarios 89
4.5.2 Implementation 91
4.5.3. IMAIS results 95
4.5.4 System usability and readability 96
CHAPTER 5 DISCUSSION 101
CHAPTER 6 CONCLUSION AND FUTURE WORK 104
REFERENCES 107
Appendix 1 113
Appendix 2 123
Appendix 3 129
Appendix 4 135
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