Breath Analysis for Medical Applications (eBook)
XIII, 309 Seiten
Springer Singapore (Verlag)
978-981-10-4322-2 (ISBN)
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.
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He is a Chair Professor since 2005 at the Hong Kong Polytechnic University where he is the Founding Director of the Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He is Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Founder and Series Editor, Springer International Series on Biometrics (KISB); Organizer, the 1st International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions and so on. He was selected as a Highly Cited Researcher in Engineering by Thomson Reuters in 2014, 2015 and 2016, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.
Dongmin Guo received her B.S. and M.S. degrees at School of Automation, Northwestern Polytechnical University Xi'an, China in 2003 and 2006, respectively and received her Ph.D. degree at the Hong Kong Polytechnic University, Hong Kong, in 2011. She is currently working as a research associate in Radiology Department, Wake Forest University Health Sciences. Her research interests include bioinformatics and machine learning.
Ke Yan received his B.S. and Ph.D. degrees both from the Department of Electronic Engineering, Tsinghua University, Beijing, China. He was the winner of the 2016 Tsinghua University Excellent Doctoral Dissertation Award. He is currently a postdoctoral fellow in the Lab of Diagnostic Radiology Research, National Institutes of Health, USA. He is studying deep learning methods to analyze medical images. His research interests include computer vision, machine learning, and their biomedical applications.
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.
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He is a Chair Professor since 2005 at the Hong Kong Polytechnic University where he is the Founding Director of the Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He is Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Founder and Series Editor, Springer International Series on Biometrics (KISB); Organizer, the 1st International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions and so on. He was selected as a Highly Cited Researcher in Engineering by Thomson Reuters in 2014, 2015 and 2016, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR. Dongmin Guo received her B.S. and M.S. degrees at School of Automation, Northwestern Polytechnical University Xi'an, China in 2003 and 2006, respectively and received her Ph.D. degree at the Hong Kong Polytechnic University, Hong Kong, in 2011. She is currently working as a research associate in Radiology Department, Wake Forest University Health Sciences. Her research interests include bioinformatics and machine learning. Ke Yan received his B.S. and Ph.D. degrees both from the Department of Electronic Engineering, Tsinghua University, Beijing, China. He was the winner of the 2016 Tsinghua University Excellent Doctoral Dissertation Award. He is currently a postdoctoral fellow in the Lab of Diagnostic Radiology Research, National Institutes of Health, USA. He is studying deep learning methods to analyze medical images. His research interests include computer vision, machine learning, and their biomedical applications.
PART I: BackgroundChapter 1: Introduction1.1Background1.2Motivation of Breath Analysis1.3Relative Technologies1.4Outline of this BookREFERENCESChapter 2: Literature Review2.1Introduction2.2Development of Breath Analysis2.3Breath Analysis by GC2.4Breath Analysis by E-nose2.5SummaryREFERENCESPART II: Breath Acquisition SystemsChapter 3: A Novel Breath Acquisition System Design3.1Introduction3.2Breath Analysis3.3Description of the System 3.4Experiments 3.5Results and Discussion 3.6SummaryREFERENCESChapter 4: An LDA Based Sensor Selection Approach4.1Introduction4.2LDA based Approach: Definition and Algorithm4.3Sensor Selection 4.4Comparison Experiment and Performance Analysis4.5SummaryREFERENCESChapter 5: Sensor Evaluation in a Breath Acquisition System5.1Introduction5.2System Description5.3Sensor Evaluation Methods 5.4Experiments and Discussion5.5SummaryREFERENCESPART III: Breath Signal Pre-ProcessingChapter 6: Improving the Transfer Ability of Prediction Models6.1Introduction6.2Methods Design6.3Experimental Details 6.4Results and Discussion6.5SummaryREFERENCESChapter 7: Learning Classification and Regression Models for Breath Data Drift based on Transfer Samples7.1Introduction7.2Related Work7.3Transfer-Sample-Based Multitask Learning (TMTL) 7.4Selection of Transfer Samples7.5Experiments7.6SummaryREFERENCESChapter 8: A Transfer Learning Approach with Autoencoder for Correcting Instrumental Variation and Time-Varying Drift8.1Introduction8.2Related Work8.3Drift Correction Autoencoder (DCAE) 8.4Selection of Transfer Samples8.5Experiments8.6SummaryREFERENCESChapter 9: A New Drift Correction Algorithm by Maximum Independence Domain Adaptation9.1Introduction9.2Related work9.3Proposed Method9.4Experiments9.5SummaryREFERENCESPART IV: Feature Extraction and ClassificationChapter 10: An Effective Feature Extraction Method for Breath Analysis10.1Introduction10.2Breath Analysis System and Breath Samples10.3Feature Extraction based on Curve-Fitting Models 10.4Experiments and Analysis10.5SummaryREFERENCESChapter 11: Feature Selection and Analysis on Correlated Breath Data11.1Introduction11.2SVM-RFE11.3Improved SVM-RFE with Correlation Bias Reduction 11.4Datasets and Feature Extraction11.5Results and Discussion11.6SummaryREFERENCESChapter 12: Breath Sample Identification by Sparse Representation-based Classification12.1Introduction12.2Sparse Representation Classification12.3Overall Procedure 12.4Experiments and Results12.5SummaryREFERENCESPART V: Medical ApplicationsChapter 13: Monitor Blood Glucose Level via Sparse Representation Approach13.1Introduction13.2System Description and Breath Signal Acquisition13.3Sparse Representation Classification 13.4Experiments and Results13.5SummaryREFERENCESChapter 14: Diabetics Detection by Means of Breath Signal Analysis14.1Introduction14.2Breath Analysis System14.3Breath Sample Classification and Decision Making14.4Experiments 14.5Results and Discussion 14.6SummaryREFERENCESChapter 15: A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction15.1Introduction15.2System Description15.3System Optimization15.4Experiments with Simulated Samples 15.5Experiments with Breath Samples15.6SummaryREFERENCESChapter 16: Book Review and Future Work16.1Book Recapitulation16.2Future Work
| Erscheint lt. Verlag | 23.6.2017 |
|---|---|
| Zusatzinfo | XIII, 309 p. 99 illus., 88 illus. in color. |
| Verlagsort | Singapore |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Medizintechnik | |
| Schlagworte | Autoencoder Learning • Blood Glucose Level Prediction • Breath signal Diagnosis • diabetes screening • Drift Correction • feature extraction • machine learning • Medical Biometrics • pattern recognition • Sensor Selection and Fusion • Signal Acquisition • Signal Preprocessing |
| ISBN-10 | 981-10-4322-1 / 9811043221 |
| ISBN-13 | 978-981-10-4322-2 / 9789811043222 |
| Haben Sie eine Frage zum Produkt? |
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