Breath Analysis for Medical Applications

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Special Collection:e-book
Format: Book
Language:English
Published: Singapore : Springer, 2017
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Online Access:https://doi.org/10.1007/978-981-10-4322-2
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id opac-EUL01-000954274
collection e-book
institution L_042
EUL01
spelling Zhang, David EUL10001009219 Y
Breath Analysis for Medical Applications by David Zhang, Dongmin Guo, Ke Yan
Singapore Springer 2017
XIII, 309 p. ill.
szöveg txt rdacontent
számítógépes c rdamedia
távoli hozzáférés cr rdacarrier
szövegfájl PDF rda
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.
Nyomtatott kiadás: ISBN 9789811043215
Nyomtatott kiadás: ISBN 9789811043239
Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők.
könyv
e-book
egészségügyi adatok adatfeldolgozás EUL10001009098 Y
optikai alakfelismerés EUL10001010632 Y
egészségügyi informatika EUL10001043007 Y
Medical records Data processing. EUL10001087178 Y
Optical pattern recognition. EUL10001087156 Y
Health Informatics. http://scigraph.springernature.com/things/product-market-codes/I23060
Pattern Recognition. http://scigraph.springernature.com/things/product-market-codes/I2203X
Signal, Image and Speech Processing. http://scigraph.springernature.com/things/product-market-codes/T24051
elektronikus könyv
Guo, Dongmin szerk. EUL10001009220 Y
Yan, Ke szerk. EUL10001009222 Y
SpringerLink (Online service) közreadó testület
Online változat https://doi.org/10.1007/978-981-10-4322-2
Singapore Springer Singapore Imprint: Springer 2017
EUL01
language English
format Book
author Zhang, David
spellingShingle Zhang, David
Breath Analysis for Medical Applications
egészségügyi adatok -- adatfeldolgozás
optikai alakfelismerés
egészségügyi informatika
Medical records -- Data processing.
Optical pattern recognition.
Health Informatics.
Pattern Recognition.
Signal, Image and Speech Processing.
elektronikus könyv
author_facet Zhang, David
Guo, Dongmin, szerk.
Yan, Ke, szerk.
SpringerLink (Online service)
author2 Guo, Dongmin, szerk.
Yan, Ke, szerk.
author_corporate SpringerLink (Online service)
author_sort Zhang, David
title Breath Analysis for Medical Applications
title_short Breath Analysis for Medical Applications
title_full Breath Analysis for Medical Applications by David Zhang, Dongmin Guo, Ke Yan
title_fullStr Breath Analysis for Medical Applications by David Zhang, Dongmin Guo, Ke Yan
title_full_unstemmed Breath Analysis for Medical Applications by David Zhang, Dongmin Guo, Ke Yan
title_auth Breath Analysis for Medical Applications
title_sort breath analysis for medical applications
publishDate 2017
publishDateSort 2017
physical XIII, 309 p. : ill.
isbn 978-981-10-4322-2
callnumber-raw 13327
callnumber-search 13327
topic egészségügyi adatok -- adatfeldolgozás
optikai alakfelismerés
egészségügyi informatika
Medical records -- Data processing.
Optical pattern recognition.
Health Informatics.
Pattern Recognition.
Signal, Image and Speech Processing.
elektronikus könyv
topic_facet egészségügyi adatok -- adatfeldolgozás
optikai alakfelismerés
egészségügyi informatika
Medical records -- Data processing.
Optical pattern recognition.
Health Informatics.
Pattern Recognition.
Signal, Image and Speech Processing.
elektronikus könyv
egészségügyi adatok
optikai alakfelismerés
egészségügyi informatika
Medical records
Optical pattern recognition.
Health Informatics.
Pattern Recognition.
Signal, Image and Speech Processing.
adatfeldolgozás
Data processing.
url https://doi.org/10.1007/978-981-10-4322-2
illustrated Illustrated
first_indexed 2021-06-16T08:47:13Z
last_indexed 2021-06-17T05:56:30Z
recordtype opac
publisher Singapore : Springer
_version_ 1702784864770588673
score 13,328153
generalnotes 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.