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
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
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