Advanced Methods of Biomedical Signal Processing: 27 (IEEE Press Series on Biomedical Engineering) - Tapa dura

Acerca del autor

Sergio Cerutti, PhD, is Chairman of the Biomedical Engineering Program at the Department of Bioengineering, Politecnico in Milan. A Fellow of the IEEE, EAMBES and AIMBE, Dr. Cerutti serves as Associate Editor of the IEEE Transactions of Biomedical Engineering. He is the Co-Chair of the Steering Committee of the International IEEE-EMBS Summer Schools on Biomedical Signal Processing.

Carlo Marchesi, PhD, is Professor of Biomedical Engineering at the University of Florence, Italy. A Co-Chair of the Italian Summer School on Biomedical Signal Processing, Dr. Marchesi's research interests include the application of computer science to medicine, data mining, and assistive technology to aid the disabled.

De la contraportada

A complete introduction to the application of advanced signal processing methods to biomedical engineering problems

This edited volume, which grew out of the GNB (Gruppo Nazionale di Bioingegneria, Italy) Summer School on Biomedical Signal Processing, explains some of the most advanced methodological signal processing techniques and applies them to biomedical engineering problems. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications.

Divided into seven sections, Advanced Methods of Biomedical Signal Processing covers:

• The peculiarities of biomedical signal processing with respect to more traditional applications of digital signal processing and their classification

• An experimental physiologist's and cardiologist's view of the cardiovascular, central and autonomic nervous systems

• An important link between biomedical signal processing and physiological modeling

• Time-frequency, time-scale, and wavelet analysis

• Advanced methods employed in complexity measurements

• Computational genomics and proteomics

• Key methods for signal classification, such as neural networks, neuro-fuzzy and genetic algorithms

The book provides a compelling overview of techniques, such as multisource and multi-scale integration of information for physiology and clinical decision; the integration of signal processing methods with a modeling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; and classification and parameter enhancement. Various contributions reveal that biomedical signal processing must be viewed in a wider context, with key links to the modeling phase of the signal-generating mechanisms, in order to better comprehend the behavior of the biological system under investigation.

Graduate and PhD students in engineering/biomedical engineering courses, physics and applied mathematics, as well as research professionals in medical and biological sciences will highly benefit from this authoritative resource.