Biomedical Signal Processing and Signal Modeling

ISBN-10: 0471345407

ISBN-13: 9780471345404

Edition: 2001

Authors: Eugene N. Bruce
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Description: Eugene N. Bruce explores a range of topics related to the acquisition and processing of biomedical signals for extracting information, and the interpretation of the nature of the physical processes that create the signals.

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Book details

List price: $219.00
Copyright year: 2001
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/4/2000
Binding: Hardcover
Pages: 536
Size: 6.50" wide x 9.50" long x 1.25" tall
Weight: 1.980
Language: English

Preface
The Nature of Biomedical Signals
The Reasons for Studying Biomedical Signal Processing
What Is a Signal?
Some Typical Sources of Biomedical Signals
Continuous-Time and Discrete-Time
Assessing the Relationships Between Two Signals
Why Do We "Process" Signals?
Types of Signals: Deterministic, Stochastic, Fractal and Chaotic
Signal Modeling as a Framework for Signal Processing
What Is Noise?
Summary
Exercises
Memory and Correlation
Introduction
Properties of Operators and Transformations
Memory in a Physical System
Energy and Power Signals
The Concept of Autocorrelation
Autocovariance and Autocorrelation for DT Signals
Summary
Exercises
The Impulse Response
Introduction
Thought Experiment and Computer Exercise: Glucose Control
Convolution Form of an LSI System
Convolution for Continuous-Time Systems
Convolution as Signal Processing
Relation of Impulse Response to Differential Equation
Convolution as a Filtering Process
Impulse Responses for Nonlinear Systems
The Glucose Control Problem Revisited
Summary
Exercises
Frequency Response
Introduction
Biomedical Example (Transducers for Measuring Knee Angle)
Sinusoidal Inputs to LTIC Systems
Generalized Frequency Response
Frequency Response of Discrete-Time Systems
Series and Parallel Filter Cascades
Ideal Filters
Frequency Response and Nonlinear Systems
Other Biomedical Examples
Summary
Exercises
Modeling Continuous-Time Signals as Sums of Sine Waves
Introduction
Introductory Example (Analysis of Circadian Rhythm)
Orthogonal Functions
Sinusoidal Basis Functions
The Fourier Series
The Frequency Response and Nonsinusoidal Periodic Inputs
Parseval's Relation for Periodic Signals
The Continuous-Time Fourier Transform (CTFT)
Relationship of Fourier Transform to Frequency Response
Properties of the Fourier Transform
The Generalized Fourier Transform
Examples of Fourier Transform Calculations
Parseval's Relation for Nonperiodic Signals
Filtering
Output Response via the Fourier Transform
Summary
Exercises
Responses of Linear Continuous-Time Filters to Arbitrary Inputs
Introduction
Introductory Example
Conceptual Basis of the Laplace Transform
Properties of (Unilateral) Laplace Transforms
The Inverse (Unilateral) Laplace Transform
Transfer Functions
Feedback Systems
Biomedical Applications of Laplace Transforms
Summary
Exercises
Modeling Signals as Sums of Discrete-Time Sine Waves
Introduction
Interactive Example: Periodic Oscillations in the Amplitude of Breathing
The Discrete-Time Fourier Series
Fourier Transform of Discrete-Time Signals
Parseval's Relation for DT Nonperiodic Signals
Output of an LSI System
Relation of DFS and DTFT
Windowing
Sampling
The Discrete Fourier Transform (DFT)
Biomedical Applications
Summary
Exercises
Noise Removal and Signal Compensation
Introduction
Introductory Example: Reducing the ECG Artifact in an EMG Recording
Eigenfunctions of LSI Systems and the Z-Transform
Properties of the Bilateral Z-Transform
Poles and Zeros of Z-Transforms
The Inverse Z-Transform
Pole Locations and Time Responses
The Unilateral Z-Transform
Analyzing Digital Filters Using Z-Transforms (DT Transfer Functions)
Biomedical Applications of DT Filters
Overview: Design of Digital Filters
IIR Filter Design by Approximating a CT Filter
IIR Filter Design by Impulse Invariance
IIR Filter Design by Bilinear Transformation
Biomedical Examples of IIR Digital Filter Design
IIR Filter Design by Minimization of an Error Function
FIR Filter Design
Frequency-Band Transformations
Biomedical Applications of Digital Filtering
Summary
Exercises
Modeling Stochastic Signals as Filtered White Noise
Introduction
Introductory Exercise: EEG Analysis
Random Processes
Mean and Autocorrelation Function of a Random Process
Stationarity and Ergodicity
General Linear Processes
Yule-Walker Equations
Autoregressive (AR) Processes
Moving Average (MA) Processes
Autoregressive-Moving Average (ARMA) Processes
Harmonic Processes
Other Biomedical Examples
Introductory Example Continued
Summary
Exercises
Scaling and Long-Term Memory
Introduction
Geometrical Scaling and Self-Similarity
Measures of Dimension
Self-Similarity and Functions of Time
Theoretical Signals Having Statistical Similarity
Measures of Statistical Similarity for Real Signals
Generation of Synthetic Fractal Signals
Fractional Differencing Models
Biomedical Examples
Summary
Exercises
Nonlinear Models of Signals
Introductory Exercise
Nonlinear Signals and Systems: Basic Concepts
Poincare Sections and Return Maps
Chaos
Measures of Nonlinear Signals and Systems
Characteristic Multipliers and Lyapunov Exponents
Estimating the Dimension of Real Data
Tests of Null Hypotheses Based on Surrogate Data
Other Biomedical Applications
Summary
Exercises
Assessing Stationarity and Reproducibility
Introduction
Assessing Stationarity of a Random Process from a Sample Function
Statistical Properties of Autocovariance Estimators
Statistical Properties of the Periodogram
Analysis of Nonstationary Signals
Nonstationary Second-Order Statistics
Summary
Exercises
Bibliography
Index
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