LEADER 03923nam 22005775 450 001 9910254822503321 005 20200706021009.0 010 $a981-10-4322-1 024 7 $a10.1007/978-981-10-4322-2 035 $a(CKB)3710000001411808 035 $a(DE-He213)978-981-10-4322-2 035 $a(MiAaPQ)EBC4884387 035 $a(PPN)202989194 035 $a(EXLCZ)993710000001411808 100 $a20170623d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBreath Analysis for Medical Applications /$fby David Zhang, Dongmin Guo, Ke Yan 205 $a1st ed. 2017. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2017. 215 $a1 online resource (XIII, 309 p. 99 illus., 88 illus. in color.) 311 $a981-10-4321-3 327 $a1. 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. 330 $aThis 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. 606 $aHealth informatics 606 $aPattern recognition 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aHealth informatics. 615 0$aPattern recognition. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aHealth Informatics. 615 24$aPattern Recognition. 615 24$aSignal, Image and Speech Processing. 676 $a502.85 700 $aZhang$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763056 702 $aGuo$b Dongmin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYan$b Ke$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910254822503321 996 $aBreath Analysis for Medical Applications$92517174 997 $aUNINA