Breath Analysis for Medical Applications / by David Zhang, Dongmin Guo, Ke Yan.

Format:

Book

Author/Creator:

Zhang, David, 1949- author.

Guo, Dongmin, author.

Yan, Ke, active 2018, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Medical informatics.

Pattern perception.

Signal processing.

Image processing.

Speech processing systems.

Health Informatics.

Pattern Recognition.

Signal, Image and Speech Processing.

Local Subjects:

Health Informatics.

Pattern Recognition.

Signal, Image and Speech Processing.

Physical Description:

1 online resource (XIII, 309 pages) : 99 illustrations, 88 illustrations in color

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Singapore : Springer Singapore : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-981-10-4322-2

9789811043222

Access Restriction:

Restricted for use by site license.