ISBN-10:
9811351066
ISBN-13:
9789811351068
Pub. Date:
12/09/2018
Publisher:
Springer Singapore
Breath Analysis for Medical Applications

Breath Analysis for Medical Applications

by David Zhang, Dongmin Guo, Ke YanDavid Zhang
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Overview

This book describes breath signal processing technologies and their applications in medical sample classification and diagnosis. First, it provides a comprehensive introduction to breath signal acquisition methods, based on different kinds of chemical sensors, together with the optimized selection and fusion acquisition scheme. It then presents preprocessing techniques, such as drift removing and feature extraction methods, and uses case studies to explore the classification methods. Lastly it discusses promising research directions and potential medical applications of computerized breath diagnosis. It is a valuable interdisciplinary resource for researchers, professionals and postgraduate students working in various fields, including breath diagnosis, signal processing, pattern recognition, and biometrics.

Product Details

ISBN-13: 9789811351068
Publisher: Springer Singapore
Publication date: 12/09/2018
Edition description: Softcover reprint of the original 1st ed. 2017
Pages: 309
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He is a Chair Professor since 2005 at the Hong Kong Polytechnic University where he is the Founding Director of the Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He is Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Founder and Series Editor, Springer International Series on Biometrics (KISB); Organizer, the 1st International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions and so on. He was selected as a Highly Cited Researcher in Engineering by Thomson Reuters in 2014, 2015 and 2016, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.


Dongmin Guo received her B.S. and M.S. degrees at School of Automation, Northwestern Polytechnical University Xi'an, China in 2003 and 2006, respectively and received her Ph.D. degree at the Hong Kong Polytechnic University, Hong Kong, in 2011. She is currently working as a research associate in Radiology Department, Wake Forest University Health Sciences. Her research interests include bioinformatics and machine learning.


Ke Yan received his B.S. and Ph.D. degrees both from the Department of Electronic Engineering, Tsinghua University, Beijing, China. He was the winner of the 2016 Tsinghua University Excellent Doctoral Dissertation Award. He is currently a postdoctoral fellow in the Lab of Diagnostic Radiology Research, National Institutes of Health, USA. He is studying deep learning methods to analyze medical images. His research interests include computer vision, machine learning, and their biomedical applications.

Table of Contents


PART I: Background


Chapter 1: Introduction
1.1 Background
1.2 Motivation of Breath Analysis
1.3 Relative Technologies
1.4 Outline of this Book
REFERENCES


Chapter 2: Literature Review
2.1 Introduction
2.2 Development of Breath Analysis
2.3 Breath Analysis by GC
2.4 Breath Analysis by E-nose
2.5 Summary
REFERENCES


PART II: Breath Acquisition Systems


Chapter 3: A Novel Breath Acquisition System Design
3.1 Introduction
3.2 Breath Analysis
3.3 Description of the System
3.4 Experiments
3.5 Results and Discussion
3.6 Summary
REFERENCES


Chapter 4: An LDA Based Sensor Selection Approach
4.1 Introduction
4.2 LDA based Approach: Definition and Algorithm
4.3 Sensor Selection
4.4 Comparison Experiment and Performance Analysis
4.5 Summary
REFERENCES


Chapter 5: Sensor Evaluation in a Breath Acquisition System
5.1 Introduction
5.2 System Description
5.3 Sensor Evaluation Methods
5.4 Experiments and Discussion
5.5 Summary
REFERENCES


PART III: Breath Signal Pre-Processing


Chapter 6: Improving the Transfer Ability of Prediction Models
6.1 Introduction
6.2 Methods Design
6.3 Experimental Details
6.4 Results and Discussion
6.5 Summary
REFERENCES


Chapter 7: Learning Classification and Regression Models for Breath Data Drift based on Transfer Samples
7.1 Introduction
7.2 Related Work
7.3 Transfer-Sample-Based Multitask Learning (TMTL)
7.4 Selection of Transfer Samples
7.5 Experiments
7.6 Summary
REFERENCES


Chapter 8: A Transfer Learning Approach with Autoencoder for Correcting Instrumental Variation and Time-Varying Drift
8.1 Introduction
8.2 Related Work
8.3 Drift Correction Autoencoder (DCAE)
8.4 Selection of Transfer Samples
8.5 Experiments
8.6 Summary
REFERENCES


Chapter 9: A New Drift Correction Algorithm by Maximum Independence Domain Adaptation
9.1 Introduction
9.2 Related work
9.3 Proposed Method
9.4 Experiments
9.5 Summary
REFERENCES


PART IV: Feature Extraction and Classification


Chapter 10: An Effective Feature Extraction Method for Breath Analysis
10.1 Introduction
10.2 Breath Analysis System and Breath Samples
10.3 Feature Extraction based on Curve-Fitting Models
10.4 Experiments and Analysis
10.5 Summary
REFERENCES


Chapter 11: Feature Selection and Analysis on Correlated Breath Data
11.1 Introduction
11.2 SVM-RFE
11.3 Improved SVM-RFE with Correlation Bias Reduction
11.4 Datasets and Feature Extraction
11.5 Results and Discussion
11.6 Summary
REFERENCES


Chapter 12: Breath Sample Identification by Sparse Representation-based Classification
12.1 Introduction
12.2 Sparse Representation Classification
12.3 Overall Procedure
12.4 Experiments and Results
12.5 Summary
REFERENCES




PART V: Medical Applications


Chapter 13: Monitor Blood Glucose Level via Sparse Representation Approach
13.1 Introduction
13.2 System Description and Breath Signal Acquisition
13.3 Sparse Representation Classification
13.4 Experiments and Results
13.5 Summary
REFERENCES


Chapter 14: Diabetics Detection by Means of Breath Signal Analysis
14.1 Introduction
14.2 Breath Analysis System
14.3 Breath Sample Classification and Decision Making
14.4 Experiments
14.5 Results and Discussion
14.6 Summary
REFERENCES


Chapter 15: A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction
15.1 Introduction
15.2 System Description
15.3 System Optimization
15.4 Experiments with Simulated Samples
15.5 Experiments with Breath Samples
15.6 Summary
REFERENCES




Chapter 16: Book Review and Future Work
16.1 Book Recapitulation
16.2 Future Work

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