rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Breath Analysis for Medical Applications
  name:
    name part: Zhang, David.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Guo, Dongmin.
    role:
      role term: author.
  name:
    name part: Yan, Ke.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: si
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 309 p. 99 illus., 88 illus. in color. online resource.
  abstract: 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.
  table of contents: 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.
  note: by David Zhang, Dongmin Guo, Ke Yan.
  subject:
    topic: Computer science
  subject:
    topic: Health informatics
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Health Informatics
  subject:
    topic: Pattern Recognition
  subject:
    topic: Signal, Image and Speech Processing
  classification: R858-R859.7
  classification: 502.85
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811043222
  identifier: http://dx.doi.org/10.1007/978-981-10-4322-2
  record info:
    record creation date: 170623
    record change date: 20170623174653.0
    record identifier: 978-981-10-4322-2