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Breath analysis for medical applications

Guo, Dongmin.

Breath analysis for medical applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Breath analysis for medical applicationsby 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$q(electronic bk.)

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$q(electronic bk.)

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

192756
Diagnosis.

LC Class. No.: RC71.6

Dewey Class. No.: 616.075

Breath analysis for medical applications
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245 1 0 $a Breath analysis for medical applications $h [electronic resource] / $c by David Zhang, Dongmin Guo, Ke Yan.
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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.
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650 0 $a Biochemical markers. $3 195446
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650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Signal, Image and Speech Processing. $3 273768
700 1 $a Guo, Dongmin. $3 787317
700 1 $a Yan, Ke. $3 787318
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