Breath Analysis for Medical Applications (Paperback)

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