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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020 |a 978-981-10-4322-2 
024 7 |a 10.1007/978-981-10-4322-2  |2 doi 
040 |a Springer  |b hun  |c ELTE 
041 0 |a eng 
080 |a 502.85 
080 |2 23 
100 1 |a Zhang, David 
245 1 0 |a Breath Analysis for Medical Applications  |c by David Zhang, Dongmin Guo, Ke Yan 
260 |a Singapore  |b Springer  |c 2017 
300 |a XIII, 309 p.  |b ill. 
336 |a szöveg  |b txt  |2 rdacontent 
337 |a számítógépes  |b c  |2 rdamedia 
338 |a távoli hozzáférés  |b cr  |2 rdacarrier 
347 |a szövegfájl  |b PDF  |2 rda 
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. 
580 |a Nyomtatott kiadás: ISBN 9789811043215 
580 |a Nyomtatott kiadás: ISBN 9789811043239 
506 |a Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. 
598 |a könyv 
595 |a e-book 
650 0 4 |a egészségügyi adatok  |x adatfeldolgozás 
650 0 4 |a optikai alakfelismerés 
650 0 4 |a egészségügyi informatika 
650 0 |a Medical records  |x Data processing. 
650 0 |a Optical pattern recognition. 
650 1 4 |a Health Informatics.  |0 http://scigraph.springernature.com/things/product-market-codes/I23060 
650 2 4 |a Pattern Recognition.  |0 http://scigraph.springernature.com/things/product-market-codes/I2203X 
650 2 4 |a Signal, Image and Speech Processing.  |0 http://scigraph.springernature.com/things/product-market-codes/T24051 
653 |a elektronikus könyv 
700 1 |a Guo, Dongmin  |e szerk. 
700 1 |a Yan, Ke  |e szerk. 
710 2 |a SpringerLink (Online service)  |e közreadó testület 
850 |a B2 
856 4 0 |y Online változat  |u https://doi.org/10.1007/978-981-10-4322-2 
264 1 |a Singapore  |b Springer Singapore  |b Imprint: Springer  |c 2017