Synopses & Reviews
Synopsis
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
Synopsis
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.