國立虎尾科技大學 |

Breath analysis for medical applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Breath analysis for medical applications/ by David Zhang, Dongmin Guo, Ke Yan.
作者:	Zhang, David.
其他作者:	Guo, Dongmin.
出版者:	Singapore :Springer Singapore : : 2017.,
面頁冊數:	xiii, 309 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Diagnosis. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-4322-2
ISBN:	9789811043222

Breath analysis for medical applications
Zhang, David.

Breath analysis for medical applications[electronic resource] /by David Zhang, Dongmin Guo, Ke Yan. - Singapore :Springer Singapore :2017. - xiii, 309 p. :ill. (some col.), digital ;24 cm.

1. Introduction -- 2. Literature Review -- 3. A Novel Breath Acquisition System Design -- 4. An LDA Based Sensor Selection Approach -- 5. Sensor Evaluation in a Breath Acquisition System -- 6. Improving the Transfer Ability of Prediction Models -- 7. Learning Classification and Regression Models for Breath Data with Drift based on Transfer Samples -- 8. A Transfer Learning Approach with Autoencoder for Correcting Instrumental Variation and Time-Varying Drift -- 9. Drift Correction using Maximum Independence Domain Adaptation -- 10. Feature Selection and Analysis on Correlated Breath Data -- 11. Breath Sample Identification by Sparse Representation-based Classification -- 12. Monitor Blood Glucose Levels via Sparse Representation Approach -- 13. Diabetics by Means of Breath Signal Analysis -- 14. A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction. 15. A Novel Medical E-Nose Signal Analysis System -- 16. Book Review and Future Work.

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.

ISBN: 9789811043222

Standard No.: 10.1007/978-981-10-4322-2doiSubjects--Topical Terms:

654481
Diagnosis.

LC Class. No.: RC71.6

Dewey Class. No.: 616.075

Breath analysis for medical applications
LDR:02740nam a2200313 a 4500 001 905596
003 DE-He213
005 20170623174653.0
006 m d
007 cr nn 008maaau
008 190308s2017 si s 0 eng d
020 $a 9789811043222 $q (electronic bk.)
020 $a 9789811043215 $q (paper)
024 7 $a 10.1007/978-981-10-4322-2 $2 doi
035 $a 978-981-10-4322-2
040 $a GP $c GP
041 0 $a eng
050 4 $a RC71.6
072 7 $a UBH $2 bicssc
072 7 $a MED000000 $2 bisacsh
082 0 4 $a 616.075 $2 23
090 $a RC71.6 $b .Z63 2017
100 1 $a Zhang, David. $3 1076935
245 1 0 $a Breath analysis for medical applications $h [electronic resource] / $c by David Zhang, Dongmin Guo, Ke Yan.
260 $a Singapore : $c 2017. $b Springer Singapore : $b Imprint: Springer,
300 $a xiii, 309 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. Literature Review -- 3. A Novel Breath Acquisition System Design -- 4. An LDA Based Sensor Selection Approach -- 5. Sensor Evaluation in a Breath Acquisition System -- 6. Improving the Transfer Ability of Prediction Models -- 7. Learning Classification and Regression Models for Breath Data with Drift based on Transfer Samples -- 8. A Transfer Learning Approach with Autoencoder for Correcting Instrumental Variation and Time-Varying Drift -- 9. Drift Correction using Maximum Independence Domain Adaptation -- 10. Feature Selection and Analysis on Correlated Breath Data -- 11. Breath Sample Identification by Sparse Representation-based Classification -- 12. Monitor Blood Glucose Levels via Sparse Representation Approach -- 13. Diabetics by Means of Breath Signal Analysis -- 14. A Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction. 15. A Novel Medical E-Nose Signal Analysis System -- 16. Book Review and Future Work.
520 $a 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.
650 0 $a Diagnosis. $3 654481
650 0 $a Breath tests. $3 1172841
650 0 $a Biochemical markers. $3 582469
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Signal, Image and Speech Processing. $3 670837
700 1 $a Guo, Dongmin. $3 1172839
700 1 $a Yan, Ke. $3 1172840
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-4322-2
950 $a Computer Science (Springer-11645)