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