Breath Analysis For Medical Applications - Zhang David; Guo Dongmin; Yan Ke | Libro Springer 12/2018 -

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zhang david; guo dongmin; yan ke - breath analysis for medical applications

Breath Analysis for Medical Applications

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Lingua: Inglese


Pubblicazione: 12/2018
Edizione: Softcover reprint of the original 1st ed. 2017


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.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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


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.

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Condizione: Nuovo
Dimensioni: 235 x 155 mm Ø 498 gr
Formato: Brossura
Illustration Notes:11 Illustrations, black and white
Pagine Arabe: 309
Pagine Romane: xiii

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