Breath Analysis for Medical Applications
Mentés helye:
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| Testületi szerző: | |
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| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
Singapore : Springer,
2017
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| Tárgyszavak: | |
| Online elérés: | https://doi.org/10.1007/978-981-10-4322-2 |
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opac-EUL01-000954274 |
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| 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), közreadó testület |
| author2 |
Guo, Dongmin, szerk. Yan, Ke, szerk. |
| author_corporate |
SpringerLink (Online service), közreadó testület |
| 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 |
2023-12-27T20:07:37Z |
| last_indexed |
2023-12-30T20:55:01Z |
| recordtype |
opac |
| publisher |
Singapore : Springer |
| _version_ |
1786737959074004993 |
| score |
13,363873 |
| 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. |