Biosignal and Medical Image Processing 3rd Semmlow Solution Manual

Product details:

ISBN-10 ‏ : ‎ 1466567368
ISBN-13 ‏ : ‎ 978-1466567368
Author: Semmlow

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.

Table contents:

Chapter 1 Introduction
1.1 Biosignals
1.2 Biosignal Measurement Systems
1.3 Transducers
1.4 Amplifier/Detector
1.5 Analog Signal Processing and Filters
1.5.1 Filter Types
1.5.2 Filter Bandwidth
1.5.3 Filter Order
1.5.4 Filter Initial Sharpness
1.6 ADC Conversion
1.6.1 Amplitude Slicing
1.6.2 Time Slicing
1.6.3 Edge Effects
1.6.4 Buffering and Real-Time Data Processing
1.7 Data Banks
1.8 Summary
Problems
Chapter 2 Biosignal Measurements, Noise, and Analysis
2.1 Biosignals
2.1.1 Signal Encoding
2.1.2 Signal Linearity, Time Invariance, Causality
2.1.2.1 Superposition
2.1.3 Signal Basic Measurements
2.1.4 Decibels
2.1.5 Signal-to-Noise Ratio
2.2 Noise
2.2.1 Noise Sources
2.2.2 Noise Properties: Distribution Functions
2.2.3 Electronic Noise
2.3 Signal Analysis: Data Functions and Transforms
2.3.1 Comparing Waveforms
2.3.1.1 Vector Representation
2.3.1.2 Orthogonality
2.3.1.3 Basis Functions
2.3.2 Correlation-Based Analyses
2.3.2.1 Correlation and Covariance
2.3.2.2 Matrix of Correlations
2.3.2.3 Cross-Correlation
2.3.2.4 Autocorrelation
2.3.2.5 Autocovariance and Cross-Covariance
2.3.3 Convolution and the Impulse Response
2.4 Summary
Problems
Chapter 3 Spectral Analysis: Classical Methods
3.1 Introduction
3.2 Fourier Series Analysis
3.2.1 Periodic Functions
3.2.1.1 Symmetry
3.2.2 Complex Representation
3.2.3 Data Length and Spectral Resolution
3.2.3.1 Aperiodic Functions
3.2.4 Window Functions: Data Truncation
3.3 Power Spectrum
3.4 Spectral Averaging: Welch’s Method
3.5 Summary
Problems
Chapter 4 Noise Reduction and Digital Filters
4.1 Noise Reduction
4.2 Noise Reduction through Ensemble Averaging
4.3 Z-Transform
4.3.1 Digital Transfer Function
4.4 Finite Impulse Response Filters
4.4.1 FIR Filter Design and Implementation
4.4.2 Derivative Filters: Two-Point Central Difference Algorithm
4.4.2.1 Determining Cutoff Frequency and Skip Factor
4.4.3 FIR Filter Design Using MATLAB
4.5 Infinite Impulse Response Filters
4.5.1 IIR Filter Implementation
4.5.2 Designing IIR Filters with MATLAB
4.6 Summary
Problems
Chapter 5 Modern Spectral Analysis: The Search for Narrowband Signals
5.1 Parametric Methods
5.1.1 Model Type and Model Order
5.1.2 Autoregressive Model
5.1.3 Yule–Walker Equations for the AR Model
5.2 Nonparametric Analysis: Eigenanalysis Frequency Estimation
5.2.1 Eigenvalue Decomposition Methods
5.2.2 Determining Signal Subspace and Noise Subspace Dimensions
5.2.3 MATLAB Implementation
5.3 Summary
Problems
Chapter 6 Time–Frequency Analysis
6.1 Basic Approaches
6.2 The Short-Term Fourier Transform: The Spectrogram
6.2.1 MATLAB Implementation of the STFT
6.3 The Wigner–Ville Distribution: A Special Case of Cohen’s Class
6.3.1 The Instantaneous Autocorrelation Function
6.3.2 Time–Frequency Distributions
6.3.3 The Analytic Signal
6.4 Cohen’s Class Distributions
6.4.1 The Choi–Williams Distribution
6.5 Summary
Problems
Chapter 7 Wavelet Analysis
7.1 Introduction
7.2 Continuous Wavelet Transform
7.2.1 Wavelet Time–Frequency Characteristics
7.2.2 MATLAB Implementation
7.3 Discrete Wavelet Transform
7.3.1 Filter Banks
7.3.1.1 Relationship between Analytical Expressions and Filter Banks
7.3.2 MATLAB Implementation
7.3.2.1 Denoising
7.3.2.2 Discontinuity Detection
7.4 Feature Detection: Wavelet Packets
7.5 Summary
Problems
Chapter 8 Optimal and Adaptive Filters
8.1 Optimal Signal Processing: Wiener Filters
8.1.1 MATLAB Implementation
8.2 Adaptive Signal Processing
8.2.1 ALE and Adaptive Interference Suppression
8.2.2 Adaptive Noise Cancellation
8.2.3 MATLAB Implementation
8.3 Phase-Sensitive Detection
8.3.1 AM Modulation
8.3.2 Phase-Sensitive Detectors
8.3.3 MATLAB Implementation
8.4 Summary
Problems
Chapter 9 Multivariate Analyses: Principal Component Analysis and Independent Component Analysis
9.1 Introduction: Linear Transformations
9.2 Principal Component Analysis
9.2.1 Determination of Principal Components Using Singular-Value Decomposition
9.2.2 Order Selection: The Scree Plot
9.2.3 MATLAB Implementation
9.2.3.1 Data Rotation
9.2.4 PCA in MATLAB
9.3 Independent Component Analysis
9.3.1 MATLAB Implementation
9.4 Summary
Problems
Chapter 10 Chaos and Nonlinear Dynamics
10.1 Nonlinear Systems
10.1.1 Chaotic Systems
10.1.2 Types of Systems
10.1.3 Types of Noise
10.1.4 Chaotic Systems and Signals
10.2 Phase Space
10.2.1 Iterated Maps
10.2.2 The Hénon Map
10.2.3 Delay Space Embedding
10.2.4 The Lorenz Attractor
10.3 Estimating the Embedding Parameters
10.3.1 Estimation of the Embedding Dimension Using Nearest Neighbors
10.3.2 Embedding Dimension: SVD
10.4 Quantifying Trajectories in Phase Space: The Lyapunov Exponent
10.4.1 Goodness of Fit of a Linear Curve
10.4.2 Methods of Determining the Lyapunov Exponent
10.4.3 Estimating the Lyapunov Exponent Using Multiple Trajectories
10.5 Nonlinear Analysis: The Correlation Dimension
10.5.1 Fractal Objects
10.5.2 The Correlation Sum
10.6 Tests for Nonlinearity: Surrogate Data Analysis
10.7 Summary Exercises
Chapter 11 Nonlinearity Detection: Information-Based Methods
11.1 Information and Regularity
11.1.1 Shannon’s Entropy Formulation
11.2 Mutual Information Function
11.2.1 Automutual Information Function
11.3 Spectral Entropy
11.4 Phase-Space-Based Entropy Methods
11.4.1 Approximate Entropy
11.4.2 Sample Entropy
11.4.3 Coarse Graining
11.5 Detrended Fluctuation Analysis
11.6 Summary
Problems
Chapter 12 Fundamentals of Image Processing: The MATLAB Image Processing Toolbox
12.1 Image-Processing Basics: MATLAB Image Formats
12.1.1 General Image Formats: Image Array Indexing
12.1.2 Image Classes: Intensity Coding Schemes
12.1.3 Data Formats
12.1.4 Data Conversions
12.2 Image Display
12.3 Image Storage and Retrieval
12.4 Basic Arithmetic Operations
12.5 Block-Processing Operations
12.5.1 Sliding Neighborhood Operations
12.5.2 Distinct Block Operations
12.6 Summary
Problems
Chapter 13 Image Processing: Filters, Transformations, and Registration
13.1 Two-Dimensional Fourier Transform
13.1.1 MATLAB Implementation
13.2 Linear Filtering
13.2.1 MATLAB Implementation
13.2.2 Filter Design
13.3 Spatial Transformations
13.3.1 Affine Transformations
13.3.1.1 General Affine Transformations
13.3.2 Projective Transformations
13.4 Image Registration
13.4.1 Unaided Image Registration
13.4.2 Interactive Image Registration
13.5 Summary
Problems
Chapter 14 Image Segmentation
14.1 Introduction
14.2 Pixel-Based Methods
14.2.1 Threshold Level Adjustment
14.2.2 MATLAB Implementation
14.3 Continuity-Based Methods
14.3.1 MATLAB Implementation
14.4 Multithresholding
14.5 Morphological Operations
14.5.1 MATLAB Implementation
14.6 Edge-Based Segmentation
14.6.1 Hough Transform
14.6.2 MATLAB Implementation
14.7 Summary
Problems
Chapter 15 Image Acquisition and Reconstruction
15.1 Imaging Modalities
15.2 CT, PET, and SPECT
15.2.1 Radon Transform
15.2.2 Filtered Back Projection
15.2.3 Fan Beam Geometry
15.2.4 MATLAB Implementation of the Forward and Inverse Radon Transforms: Parallel Beam Geometry
15.2.5 MATLAB Implementation of the Forward and Inverse Radon Transforms: Fan Beam Geometry
15.3 Magnetic Resonance Imaging
15.3.1 Magnetic Gradients
15.3.2 Data Acquisition: Pulse Sequences
15.4 Functional MRI
15.4.1 fMRI Implementation in MATLAB
15.4.2 Principal Component and ICA
15.5 Summary
Problems
Chapter 16 Classification I: Linear Discriminant Analysis and Support Vector Machines
16.1 Introduction
16.1.1 Classifier Design: Machine Capacity
16.2 Linear Discriminators
16.3 Evaluating Classifier Performance
16.4 Higher Dimensions: Kernel Machines
16.5 Support Vector Machines
16.5.1 MATLAB Implementation
16.6 Machine Capacity: Overfitting or “Less Is More”
16.7 Extending the Number of Variables and Classes
16.8 Cluster Analysis
16.8.1 K-Nearest Neighbor Classifier
16.8.2 k-Means Clustering Classifier
16.9 Summary
Problems
Chapter 17 Classification II: Adaptive Neural Nets
17.1 Introduction
17.1.1 Neuron Models
17.2 Training the McCullough–Pitts Neuron
17.3 The Gradient Decent Method or Delta Rule
17.4 Two-Layer Nets: Back Projection
17.5 Three-Layer Nets
17.6 Training Strategies
17.6.1 Stopping Criteria: Cross-Validation
17.6.2 Momentum
17.7 Multiple Classifications
17.8 Multiple Input Variables
17.9 Summary
Problems
Appendix A: Numerical Integration in MATLAB
Appendix B: Useful MATLAB Functions
Bibliography
Index

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