Computational Pulse Signal Analysis / by David Zhang, Wangmeng Zuo, Peng Wang.

• 1. Introduction: Computational Pulse Diagnosis -- 2. Compound Pressure Signal Acquisition -- 3. Pulse Signal Acquisition Using Multi-Sensors -- 4. Baseline Wander Correction in Pulse Waveforms Using Wavelet-Based Cascaded Adaptive Filter -- 5. Detection of Saturation And Artifact -- 6. Optimized Preprocessing Framework for Wrist Pulse Analysis -- 7. Arrhythmic Pulses Detection -- 8. Spatial and Spectrum Feature Extraction -- 9. Generalized Feature Extraction for Wrist Pulse Analysis: from 1-D Time Series to 2-D Matrix -- 10. Characterization of Inter-Cycle Variations for Wrist Pulse Diagnosis -- 11. Edit Distance for Pulse Diagnosis -- 12. Modified Gaussian Models and Fuzzy C-Means -- 13. Modified Auto-Regressive Models -- 14. Combination of Heterogeneous Features for Wrist Pulse Blood Flow Signal Diagnosis via Multiple Kernel Learning -- 15. Comparison of Three Different Types of Wrist Pulse Signals -- 16. Comparison Between Pulse And Ecg -- 17. Disscusion and Future Work.

• This book describes the latest advances in pulse signal analysis and their applications in classification and diagnosis. First, it provides a comprehensive introduction to useful techniques for pulse signal acquisition based on different kinds of pulse sensors together with the optimized acquisition scheme. It then presents a number of preprocessing and feature extraction methods, as well as case studies of the classification methods used. Lastly it discusses some promising directions for the future study and clinical applications of pulse signal analysis. The book is a valuable resource for researchers, professionals and postgraduate students working in the field of pulse diagnosis, signal processing, pattern recognition and biometrics. It is also useful for those involved in interdisciplinary research. 

Ämnesord

Optical pattern recognition.  (LCSH)

Medical records  -- Data processing. (LCSH)

Pattern Recognition. 

Signal, Image and Speech Processing. 

Health Informatics. 

Klassifikation

Q337.5 (LCC)

TK7882.P3 (LCC)

COM016000 (ämneskategori)

UYQP (ämneskategori)

006.4 (DDC)

Pueam (kssb/8 (machine generated))