PART I: Background
Chapter 1: Introduction1.1 Background 1.2 Motivation of Breath Analysis 1.3 Relative Technologies 1.4 Outline of this BookREFERENCES
Chapter 2: Literature Review2.1 Introduction 2.2 Development of Breath Analysis 2.3 Breath Analysis by GC 2.4 Breath Analysis by E-nose 2.5 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES
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 SummaryREFERENCES

PART V: Medical Applications
Chapter 13: Monitor Blood Glucose Level v
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 1^st 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.