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Table of Contents
Preface; Contents; Background; 1 Introduction; 1.1 Background and Motivation; 1.1.1 Why Is Breath Analysis Used in Disease Diagnosis?; 1.1.2 Why Should Breath Analysis System Be Developed?; 1.1.3 Why Should Specific Algorithms Be Designed for Breath Analysis?; 1.2 Relative Technologies; 1.3 Outline of the Work; References; 2 Literature Review; 2.1 Introduction; 2.2 Development of Breath Analysis; 2.3 Breath Analysis by GC; 2.3.1 Lung Cancer; 2.3.2 Lipid Peroxidation; 2.3.3 Renal Diseases; 2.3.4 Liver Diseases; 2.3.5 Breast Cancer; 2.3.6 Diabetes; 2.3.7 Pulmonary Tuberculosis; 2.3.8 Summary
2.4 Breath Analysis by E-Nose2.5 Summary; References; Breath Acquisition Systems; 3 A Novel Breath Acquisition System Design; 3.1 Introduction; 3.2 Breath Analysis; 3.3 Description of the System; 3.3.1 Breath Gas Collecting; 3.3.2 Signal Sampling; 3.3.3 Data Analysis; 3.4 Experiments; 3.4.1 Evaluating Outcomes of Hemodialysis; 3.4.2 Distinguishing Between Subject Breath Samples; 3.5 Results and Discussion; 3.5.1 Results Evaluating Outcomes of Hemodialysis; 3.5.2 Results Distinguishing Between Subject Breath Samples; 3.6 Summary; References; 4 An LDA-Based Sensor Selection Approach
4.1 Introduction4.2 LDA-Based Approach: Definition and Algorithm; 4.2.1 Data Expression; 4.2.2 Find Out the Optimum Direction by LDA; 4.2.3 Difference Between Two Classes as the Linear Combination of Sensors; 4.2.4 Weight of Sensor; 4.2.5 Algorithm Conclusion; 4.3 Sensor Selection in Breath Analysis System; 4.3.1 Sensor Selection for Disease Diagnosis; 4.3.2 Evaluating the Medical Treatment; 4.4 Comparison Experiment and Performance Analysis; 4.4.1 Sensor Selection for Disease Diagnosis; 4.4.2 Evaluating the Medical Treatment; 4.5 Summary; References
5 Sensor Evaluation in a Breath Acquisition SystemAbstract; 5.1 Introduction; 5.2 System Description; 5.2.1 Framework of the Device; 5.2.2 Sensor Array; 5.2.3 Sampling Procedure; 5.2.4 Data Analysis; 5.3 Sensor Evaluation Methods; 5.3.1 Cumulative Sensor Importance; 5.3.2 Average Accuracy Improvement; 5.3.3 Sensor Inter-relationship; 5.4 Experiments and Discussion; 5.4.1 Experiment Configuration; 5.4.2 Sensor Evaluation Results; 5.4.3 Discussion; 5.5 Summary; References; Breath Signal Pre-processing; 6 Improving the Transfer Ability of Prediction Models; 6.1 Introduction
6.2 Design of Methods6.2.1 Windowed Piecewise Direct Standardization (WPDS); 6.2.2 Standardization-Error-Based Model Improvement (SEMI); 6.3 Experimental Details; 6.3.1 E-nose Module; 6.3.2 Dataset; 6.3.3 Preprocessing and Feature Extraction; 6.3.4 Data Analysis Procedure; 6.4 Results and Discussion; 6.4.1 Standardization; 6.4.2 Prediction; 6.5 Summary; References; 7 Learning Classification and Regression Models Based on Transfer Samples; Abstract; 7.1 Introduction; 7.2 Related Work; 7.3 Transfer-Sample-Based Multitask Learning (TMTL); 7.3.1 Transfer-Sample-Based Coupled Task Learning (TCTL)
2.4 Breath Analysis by E-Nose2.5 Summary; References; Breath Acquisition Systems; 3 A Novel Breath Acquisition System Design; 3.1 Introduction; 3.2 Breath Analysis; 3.3 Description of the System; 3.3.1 Breath Gas Collecting; 3.3.2 Signal Sampling; 3.3.3 Data Analysis; 3.4 Experiments; 3.4.1 Evaluating Outcomes of Hemodialysis; 3.4.2 Distinguishing Between Subject Breath Samples; 3.5 Results and Discussion; 3.5.1 Results Evaluating Outcomes of Hemodialysis; 3.5.2 Results Distinguishing Between Subject Breath Samples; 3.6 Summary; References; 4 An LDA-Based Sensor Selection Approach
4.1 Introduction4.2 LDA-Based Approach: Definition and Algorithm; 4.2.1 Data Expression; 4.2.2 Find Out the Optimum Direction by LDA; 4.2.3 Difference Between Two Classes as the Linear Combination of Sensors; 4.2.4 Weight of Sensor; 4.2.5 Algorithm Conclusion; 4.3 Sensor Selection in Breath Analysis System; 4.3.1 Sensor Selection for Disease Diagnosis; 4.3.2 Evaluating the Medical Treatment; 4.4 Comparison Experiment and Performance Analysis; 4.4.1 Sensor Selection for Disease Diagnosis; 4.4.2 Evaluating the Medical Treatment; 4.5 Summary; References
5 Sensor Evaluation in a Breath Acquisition SystemAbstract; 5.1 Introduction; 5.2 System Description; 5.2.1 Framework of the Device; 5.2.2 Sensor Array; 5.2.3 Sampling Procedure; 5.2.4 Data Analysis; 5.3 Sensor Evaluation Methods; 5.3.1 Cumulative Sensor Importance; 5.3.2 Average Accuracy Improvement; 5.3.3 Sensor Inter-relationship; 5.4 Experiments and Discussion; 5.4.1 Experiment Configuration; 5.4.2 Sensor Evaluation Results; 5.4.3 Discussion; 5.5 Summary; References; Breath Signal Pre-processing; 6 Improving the Transfer Ability of Prediction Models; 6.1 Introduction
6.2 Design of Methods6.2.1 Windowed Piecewise Direct Standardization (WPDS); 6.2.2 Standardization-Error-Based Model Improvement (SEMI); 6.3 Experimental Details; 6.3.1 E-nose Module; 6.3.2 Dataset; 6.3.3 Preprocessing and Feature Extraction; 6.3.4 Data Analysis Procedure; 6.4 Results and Discussion; 6.4.1 Standardization; 6.4.2 Prediction; 6.5 Summary; References; 7 Learning Classification and Regression Models Based on Transfer Samples; Abstract; 7.1 Introduction; 7.2 Related Work; 7.3 Transfer-Sample-Based Multitask Learning (TMTL); 7.3.1 Transfer-Sample-Based Coupled Task Learning (TCTL)