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Preface -- Contributors -- Part I. Fundamentals of Biomedical Signal Processing and Introduction to Advanced Methods -- 1. Methods of Biomedical Signal Processing -- Multiparametric and Multidisciplinary Integration toward a Better Comprehension of Pathophysiological Mechanisms (Sergio Cerutti) -- 2. Data, Signals, and Information -- Medical Applications of Digital Signal Processing (Carlo Marchesi, Matteo Paoletti, and Loriano Galeotti) -- Part II. Points of View of the Physiologist and Clinician -- 3. Methods and Neurons (Gabriele E. M. Biella) -- 4. Evaluation of the Autonomic Nervous System -- From Algorithms to Clinical Practice (Maria Teresa La Rovere) -- Part III. Models and Biomedical Signals -- 5. Parametric Models for the Analysis of Interactions in Biomedical Signals (Giuseppe Baselli, Alberto Porta, and Paolo Bolzern) -- 6. Use of Interpretative Models in Biological Signal Processing (Mauro Ursino) -- 7. Multimodal Integration of EEG, MEG, and Functional MRI in the Study of Human Brain Activity (Fabio Babiloni, Fabrizio De Vico Fallani, and Febo Cincotti) -- 8. Deconvolution for Physiological Signal Analysis (Giovanni Sparacino, Gianluigi Pillonetto, Giuseppe De Nicolao, and Claudio Cobelli) -- Part IV. Time-Frequency, Time-Scale, and Wavelet Analysis -- 9. Linear Time-Frequency Representation (Maurizio Varanini) -- 10. Quadratic Time-Frequency Representation (Luca Mainardi) -- 11. Time-Variant Spectral Estimation (Anna M. Bianchi) -- Part V. Complexity Analysis and Nonlinear Methods -- 12. Dynamical Systems and Their Bifurcations (Fabio Dercole and Sergio Rinaldi) -- 13. Fractal Dimension -- From Geometry to Physiology (Rita Balocchi) -- 14. Nonlinear Analysis of Experimental Time Series (Maria Gabriella Signorini and Manuela Ferrario) -- 15. Blind Source Separation -- Application to Biomedical Signals (Luca Mesin, Ale�S Holobar, and Roberto Merletti) -- 16. Higher Order Spectra (Giovanni Calcagnini and Federica Censi) -- Part VI. Information Processing of Molecular Biology Data.

17. Molecular Bioengineering and Nanobioscience -- Data Analysis and Processing Methods (Carmelina Ruggiero) -- 18. Microarray Data Analysis -- General Concepts, Gene Selection, and Classification (Ricardo Bellazzi, Silvio Bicciato, Claudio Cobelli, Barbara Di Camillo, Fulvia Ferraazzi, Paolo Magni, Licia Sacchi, and Gianna Toffolo) -- 19. Microarray Data Analysis -- Gene Regulatory Networks (Riccardo Bellazzi, Silvio Bicciato, Claudio Cobelli, Barbara Di Camillo, Fulvia Ferrazzi, Paolo Magni, Lucia Sacchi, and Gianna Toffolo) -- 20. Biomolecular Sequence Analysis (Linda Pattini and Sergio Cerutti) -- Part VII. Classification and Feature Extraction -- 21. Soft Computing in Signal and Data Analysis -- Neural Networks, Neuro-Fuzzy Networks, and Genetic Algorithms (Giovanni Magenes, Francesco Lunghi, and Stefano Ramat) -- 22. Interpretation and Classification of Patient Status Patterns (Matteo Paoletti and Carlo Marchesi) -- Index -- IEEE Press Series in Biomedical Engineering.

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A complete introduction to the application of advanced signal processing methods to biomedical engineering problemsThis 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 algorithmsThe 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.

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