Biosignal and Medical Image Processing, Third Edition

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Introduction Biosignals Biosignal Measurement Systems Transducers Amplifier/Detector Analog Signal Processing and Filters ADC Conversion Data Banks Summary Problems Biosignal Measurements, Noise, and Analysis Biosignals Noise Signal Analysis: Data Functions and Transforms Summary Problems Spectral Analysis: Classical Methods Introduction Fourier Series Analysis Power Spectrum Spectral Averaging: Welch's Method Summary Problems Noise Reduction and Digital Filters Noise Reduction Noise Reduction through Ensemble Averaging Z-Transform Finite Impulse Response Filters Infinite Impulse Response Filters Summary Problems Modern Spectral Analysis: The Search for Narrowband Signals Parametric Methods Nonparametric Analysis: Eigenanalysis Frequency Estimation Problems TimeFrequency Analysis Basic Approaches The Short-Term Fourier Transform: The Spectrogram The WignerVille Distribution: A Special Case of Cohen's Class Cohen's Class Distributions Summary Problems Wavelet Analysis Introduction Continuous Wavelet Transform Discrete Wavelet Transform Feature Detection: Wavelet Packets Summary Problems Optimal and Adaptive Filters Optimal Signal Processing: Wiener Filters 8.2 Adaptive Signal Processing 8.3 Phase-Sensitive Detection 8.4 Summary Problems Multivariate Analyses: Principal Component Analysis and Independent Component Analysis Introduction: Linear Transformations Principal Component Analysis Independent Component Analysis Summary Problems Chaos and Nonlinear Dynamics Nonlinear Systems Phase Space Estimating the Embedding Parameters Quantifying Trajectories in Phase Space: The Lyapunov Exponent Nonlinear Analysis: The Correlation Dimension Tests for Nonlinearity: Surrogate Data Analysis Summary Exercises Nonlinearity Detection: Information-Based Methods Information and Regularity Mutual Information Function Spectral Entropy Phase-Space-Based Entropy Methods Detrended Fluctuation Analysis Summary Problems Fundamentals of Image Processing: The MATLAB Image Processing Toolbox Image-Processing Basics: MATLAB Image Formats Image Display Image Storage and Retrieval Basic Arithmetic Operations Block-Processing Operations Summary Problems Image Processing: Filters, Transformations, and Registration Two-Dimensional Fourier Transform Linear Filtering Spatial Transformations Image Registration Summary Problems Image Segmentation Introduction Pixel-Based Methods Continuity-Based Methods Multithresholding Morphological Operations Edge-Based Segmentation Summary Problems Image Acquisition and Reconstruction Imaging Modalities CT, PET, and SPECT Magnetic Resonance Imaging Functional MRI Summary Problems Classification I: Linear Discriminant Analysis and Support Vector Machines Introduction Linear Discriminators Evaluating Classifier Performance Higher Dimensions: Kernel Machines Support Vector Machines Machine Capacity: Overfitting or "Less Is More" Extending the Number of Variables and Classes Cluster Analysis Summary Problems Classification II: Adaptive Neural Nets Introduction Training the McCulloughPitts Neuron The Gradient Decent Method or Delta Rule Two-Layer Nets: Back Projection Three-Layer Nets Training Strategies Multiple Classifications Multiple Input Variables Summary Problems Appendix A: Numerical Integration in MATLAB Appendix B: Useful MATLAB Functions Bibliography Index
Written specifically for biomedical engineers, Biosignal and Medical Image Processing, Third Edition provides a complete set of signal and image processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. A full set of PowerPoint slides covering the material in each chapter and problem solutions is available to instructors for download. See What's New in the Third Edition: Two new chapters on nonlinear methods for describing and classifying signals. Additional examples with biological data such as EEG, ECG, respiration and heart rate variability Nearly double the number of end-of-chapter problems MATLAB(R) incorporated throughout the text Data "cleaning" methods commonly used in such areas as heart rate variability studies The text provides a general understanding of image processing sufficient to allow intelligent application of the concepts, including a description of the underlying mathematical principals when needed.
Throughout this textbook, signal and image processing concepts are implemented using the MATLAB(R) software package and several of its toolboxes. The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering education, particularly at the graduate level where a comprehensive education must be attained with a minimum number of courses. This has led to the development of "core" courses to be taken by all students. This text was written for just such a core course. It is also suitable for an upper-level undergraduate course and would also be of value for students in other disciplines that would benefit from a working knowledge of signal and image processing.

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Autor: John L. Semmlow
ISBN-13 :: 9781466567368
ISBN: 1466567368
Erscheinungsjahr: 25.02.2014
Verlag: CRC PR INC
Gewicht: 1225g
Seiten: 630
Sprache: Englisch
Auflage 00003, Revised
Sonstiges: Buch, 254x175x33 mm
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