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研究生:廖育萱
研究生(外文):Yu-Hsuan Liao
論文名稱:開發電子鼻系統使用傳感器陣列並經由機器學習方式以預測重症監護病房中的呼吸機相關性肺炎
論文名稱(外文):Development of an E-nose system using sensor array via machine learning methods to predict ventilator-associated pneumonia in intensive care units
指導教授:謝建興
指導教授(外文):Jiann-Shing Shieh
口試委員:沈家傑江右君謝建興施崇鴻范守仁
口試委員(外文):Chia-Chieh ShenYu-Chun ChiangJiann-Shing ShiehChung-Hung ShihShou-Zen Fan
口試日期:2020-06-19
學位類別:博士
校院名稱:元智大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:113
中文關鍵詞:呼吸器相關肺炎電子鼻重症加護室支持向量機人工神經網絡
外文關鍵詞:ventilator-associated pneumoniaelectronic noseintensive care unitsupport vector machineartificial neural network
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本研究主要關注病患在重症加護病房中使用呼呼器後離線檢測肺炎感染情況。因此,提出了用於呼吸機相關性肺炎(VAP)快速診斷的機器學習方法。先前研究的設備Cyranose 320 e-nose通常用於研究肺部相關疾病,它是一種高度集成的系統和傳感器,包括使用聚合物和炭黑材料的32個陣列組成的感測器。在本論文的第一項研究中,總共涉及24名受試者,其中12名感染了肺炎的受試者,其餘未感染。將工神經網絡和支持向量機(SVM)方法應用於患者數據,以預測他們是否感染了銅綠假單胞菌感染的VAP。此外,為了提高預測模型的準確性和通用性,應用了集成神經網絡(ENN)方法。在本研究中,對ENN和SVM預測模型進行了訓練和測試。為了評估模型的性能,應用了五重交叉驗證方法。結果表明,ENN和SVM模型均對綠膿桿菌感染的VAP具有較高的識別率。
在本論文的第二項研究中,開發了帶有28個金屬氧化物半導體氣體傳感器的電子鼻(e-nose),用於預測將患者插入重症監護室後的感染情況。使用臨床數據驗證了VAP識別的有效性。本研究共納入40位患者,其中20位感染了銅綠假單胞菌,其餘未感染。結果表明,分別通過SVM和ANN模型獲得了良好的精度。這項研究為在早期階段快速篩選VAP提供了一種簡單,低成本的解決方案。
綜上所述,本文介紹了研究設計的電子鼻,包括MOX型氣體傳感器芯片,系統設計以及使用ANN / SVM演算法快速篩檢ICU中的肺炎感染和非感染病患。這項研究的主要成就是,開發一種低成本與高可靠度對於早期VAP快篩之電子鼻系統。

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In the 1st study of this thesis, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models’ performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection.
In the 2nd study of this thesis, an electronic nose (e-nose) with 28 metal oxide semiconductor gas sensors was developed for predicting the presence of infection after patients have been intubated in the intensive care unit. The effectiveness of VAP identification was verified using clinical data. A total of 40 patients were included in this study, of whom 20 were infected with Pseudomonas aeruginosa and the remaining were uninfected. The results revealed that good accuracy were achieved by SVM and ANN models, respectively. This study provides a simple, low-cost solution for the rapid screening of VAP at an early stage.
In summary, this thesis presents the research design e-nose including MOX type gas sensor chip, system design and using ANN/ SVM to screen pneumonia infection and non-infection in ICU. The main achievement of this study is the importance of developing high reliability of an e-nose system for a simple, low-cost solution for the rapid screening of VAP at an early stage.

書名頁 i
英文摘要 ii
中文摘要 iv
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 xii
Chapter 1 Introduction 1
Chapter 2 Literature reviews 5
2.1 Basic structure of the electronic nose system 5
2.2 Chemical resistance transducer 7
2.3 Conductive polymer gas sensor 8
2.4 Metal oxide semiconductor gas sensor 10
2.5 Sensor interface and control circuit 16
2.6 Chapter summary 18
Chapter 3 Fabrication Resistive Type Micro Ammonia Gas Sensor Using Bilayer SnO2/WO3 Films 20
3.1 Introduction 20
3.2 Sensor structure and MEMS process 22
3.3 X-ray diffraction pattern 24
3.4 Bilayer film morphologies structure 25
3.5 Experimental result of performance test 28
3.6 Chapter summary 31
Chapter 4 Machine learning method applied to predict ventilator-associated pneumonia with Pseudomonas Aeruginosa infection via sensor array of electronic nose in intensive care unit 33
4.1 Introduction 33
4.2 Exhaled breath database 35
4.3 Sensor array sensitivity analysis 38
4.4 Data preprocess 42
4.5 Architecture of ensemble neural networks 43
4.6 Architecture of SVM 45
4.7 Evaluate the SVM predictive performance 47
4.7.1 Prediction ability of the ENN model 48
4.7.2 Prediction ability of the SVM model 49
4.8 ROC curve analysis 50
4.9 Chapter summary 52
Chapter 5 Using artificial neural network to predict a variety of pathogenic microorganisms 53
5.1 Introduction 53
5.2 The pneumonia detection system 55
5.3 Sensors 55
5.4 Data source 57
5.5 Data pre-processing 58
5.6 Sensitivity analysis of sensors 59
5.7 Machine learning 60
5.8 Experimental conclusions 62
5.9 Chapter summary 65
Chapter 6 Development of an e-nose system using machine learning methods to predict ventilator-associator pneumonia 66
Chapt 6.1 Introduction 66
6.2 Hardware implementation 68
6.2.1 Sensor array panel 68
6.2.2 E-nose circuit panel 69
6.3 Breath samples and collection method 72
6.4 Detection process 75
6.5 Machine learning methods 78
6.6 Data analysis and results 80
6.6.1 Data Preprocessing 80
6.6.2 Predictive ability of ANN and SVM ROC models 80
6.7 Chapter Summary 83
Chapter 7 Conclusion and Future Works 85
References 88

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