Breath Analysis for Medical Applications

TRAMA

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.

SOMMARIO

PART I: BackgroundChapter 1: Introduction1.1 Background 1.2 Motivation of Breath Analysis 1.3 Relative Technologies 1.4 Outline of this BookREFERENCESChapter 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 SummaryREFERENCESPART II: Breath Acquisition SystemsChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESPART III: Breath Signal Pre-ProcessingChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESPART IV: Feature Extraction and ClassificationChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESPART V: Medical ApplicationsChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCESChapter 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 SummaryREFERENCES<Chapter 16: Book Review and Future Work 16.1 Book Recapitulation 16.2 Future Work

AUTORE

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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9789811351068
• Dimensioni: 235 x 155 mm Ø 498 gr
• Formato: Brossura
• Illustration Notes: XIII, 309 p. 99 illus., 88 illus. in color.
• Pagine Arabe: 309
• Pagine Romane: xiii