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E-grāmata: Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling

  • Formāts: PDF+DRM
  • Sērija : Topics in Biomedical Engineering
  • Izdošanas datums: 06-Dec-2012
  • Izdevniecība: Kluwer Academic/Plenum Publishers
  • Valoda: eng
  • ISBN-13: 9781461542995
  • Formāts - PDF+DRM
  • Cena: 160,60 €*
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  • Formāts: PDF+DRM
  • Sērija : Topics in Biomedical Engineering
  • Izdošanas datums: 06-Dec-2012
  • Izdevniecība: Kluwer Academic/Plenum Publishers
  • Valoda: eng
  • ISBN-13: 9781461542995

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This book fills a critical gap in biomedical data analysis in making the connection between signal processing and physiological modeling. Based on the premise that the use of signal processing techniques is predicated on explicit or implicit models, this book provides a foundation in systems analysis and signal processing techniques for physiological data. The book comprises two main parts: namely, signal processing techniques for linear systems, and physiological modeling. Beginning with a broad introduction to signals and systems, the book proceeds to contemporary techniques in digital signal processing. While maintaining continuity of mathematical concepts, the emphasis is on practical implementation and applications. The signal processing topics covered include Fourier transform, the wavelet transform, and optimal filtering techniques. The book presumes only knowledge of college mathematics and is suitable for a beginner in the subject; however, a student with a previous course in analog and digital signal processing will find that only a third of the book contains a bare treatment of classical signal processing. The extensive use of diagrams illustrates the graphical nature of modern signal processing, and provides easy descriptions of practical techniques and their shortcomings. Each chapter has a number of illustrative examples and exercises. The accompanying software provides exercises in convolution, sampling, Fourier analysis and wavelet decomposition that illustrate the use of these techniques as well as their shortcomings. The latter part of the book discusses techniques of physiological modeling, contrasting biophysical models with black-box models, and experimental procedures used in such modeling. Model-based data analysis including noise reduction and feature extraction in physiology are discussed in detail. Several numerical simulation exercises are also outlined for the student.

Recenzijas

'The book...fills a void in available textbooks. It is ideally suited for a college senior or first year graduate course in bioengineering, physiology or biophysics. I recommend it.' James C. Lin, Professor of Bioengineering, University of Illinois at Chicago 'The new book...is a delight to read. It is well organized, packed with useful analytical tools, and replete with relevant biomedical examples. Devasahayam is an articulate author who has taken pains to ensure that the material will be accessible to those with engineering as well as biological backgrounds. The book is highly recommended.' Gerald H. Pollack, Professor of Bioengineering, University of Washington 'This book by Suresh R. Devasahayam is a wonderful introduction to signal processing and system modeling for students who are either pursuing or wished they had pursued a degree in biomedical engineering. The basic concepts of signal processing and system modeling are clearly explained and are elucidated with a number of exercises and applications.' Carlo De Luca, Director Neuromuscular Research Center, Boston University

Papildus informācija

Springer Book Archives
Introduction to Systems Analysis and Numerical Methods
1(10)
The Systems Approach to Physiological Analysis
1(2)
Physiological Signals and Systems
2(1)
Linear Systems Modeling in Physiology
3(1)
Numerical Methods for Data Analysis and Simulation
3(5)
Numerical Integration and Differentiation
4(3)
Graphical Display
7(1)
Examples of Physiological Models
8(3)
Exercises
9(2)
Continous Time Signals and Systems
11(20)
Physiological Measurement and Analysis
11(1)
Time Signals
12(4)
Examples of Physiological Signals
12(1)
Operations on Time Signals
13(3)
Input-Output Systems
16(15)
Properties of Systems
16(2)
Linear Time-Invariant Systems
18(2)
Impulse Response of a Linear Time-Invariant System
20(1)
The Convolution Integral
21(1)
Properties of Convolution
22(8)
Exercises
30(1)
Fourier Analysis for Continuous Time Processes
31(36)
Decomposition of Periodic Signals
31(5)
Synthesis of an ECG Signal Using Pure Sinusoids
32(4)
Fourier Conversions
36(10)
Periodic Continuous Time Signals: Fourier Series
36(6)
Aperiodic Continuous Time Signals: Fourier Transform
42(2)
Properties of the Fourier Domain
44(2)
System Transfer Function
46(18)
The Laplace Transform
47(1)
Properties of the Laplace Transform
47(5)
Frequency Response of LTI Systems
52(1)
Pole-Zero Plots and Bode Plots
53(9)
Frequency Filters
62(2)
Phase Shifts and Time Delays
64(1)
Systems Representation of Physiological Processes
64(3)
Exercises
65(2)
Discrete Time Signals and Systems
67(34)
Discretization of Continuous-Time Signals
67(10)
Sampling and Quantization
68(1)
The Sampling Theorem
69(4)
Reconstruction of a Signal from Its Sampled Version
73(1)
Quantization of Sampled Data
74(1)
Data Conversion Time-Sample and Hold
75(2)
Discrete-Time Signals
77(1)
Analogue to Digital Conversion
77(1)
Operations on Discrete-Time Signals
77(1)
Discrete-Time Systems
78(9)
The Impulse Response of a Discrete LTI System
79(1)
The Convolution Sum
79(1)
Properties of the Discrete Convolution Operation
80(1)
Examples of the Convolution Sum
80(1)
Frequency Filtering by Discrete-Time Systems
81(5)
Determination of Impulse Response from I/O Relation
86(1)
Random Signals
87(14)
Statistical Descriptions of Random Signals
91(1)
Ensemble Average and Time Average
92(2)
Stationary Processes
94(1)
Auto-correlation and Cross-Correlation of Discrete Signals
95(2)
Exercises
97(1)
Programming Exercise
98(3)
Fourier Analysis for Discrete-Time Processes
101(38)
Discrete Fourier Conversions
101(11)
Periodic Discrete Time Signals: Discrete Fourier Series
101(1)
Aperiodic Discrete-Time Signals: DTFT
102(4)
Numerical Implementation of Fourier Conversion: DFT
106(3)
Inter-Relations among Fourier Conversions
109(3)
Applying the Discrete Fourier Transform
112(10)
Properties of the DFT
112(3)
Windowing
115(2)
The Fast Fourier Transform
117(3)
Convolution Using the FFT-Circular Convolution
120(2)
The Z-Transform
122(5)
Properties of the Z-transform
122(3)
The Bilinear Transformation
125(2)
Discrete Fourier Transform of Random Signals
127(12)
Estimating the Power Spectrum
127(2)
Transfer Function Estimation or System Identification
129(1)
Exercises
130(2)
Programming Exercises
132(7)
Time-Frequency and Wavelet Analysis
139(26)
Time-Varying Processes
139(1)
The Short Time Fourier Transform
140(3)
The Continuous Time STFT and the Gabor Transform
142(1)
Wavelet Decomposition of Signals
143(9)
Multi-Resolution Decomposition
144(1)
Hierarchical Filter Bank for Wavelet Decomposition
145(2)
The Daubechies 4-Coefficient Wavelet Filters
147(5)
The Wavelet Transform
152(9)
Interpretation of the Wavelet Transform
157(1)
The Inverse Wavelet Transform
158(3)
Comparison of Fourier and Wavelet Transforms
161(4)
Exercises
164(1)
Estimation of Signals in Noise
165(16)
Noise Reduction by Filtering
165(7)
Mean Square Error Minimization
166(3)
Optimal Filtering
169(3)
Time Series Analysis
172(9)
Systems with Unknown Inputs-Autoregressive Model
173(1)
Time-Series Model Estimation
174(3)
Recursive Identification of a Non-Stationary Model
177(2)
Time-Series Modeling and Estimation in Physiology
179(1)
Exercises
179(2)
Feedback Systems
181(16)
Physiological Systems with Feedback
181(2)
Analysis of Feedback Systems
183(10)
Advantages of Feedback Control
184(5)
Analysis of Closed-Loop System Stability using Bode Plots
189(4)
Digital Control in Feedback Systems
193(4)
Exercises
195(2)
Model Based Analysis of Physiological Signals
197(12)
Modeling Physiological Systems
197(3)
Biophysical Models and Black Box Models
197(1)
Purpose of Physiological Modeling and Signal Analysis
198(1)
Linearization of Nonlinear Models
198(2)
Validation of Model Behavior against Experiment
200(1)
Model Based Noise Reduction and Feature Extraction
200(9)
Time Invariant System with Measurable Input-Output
201(2)
Time-Invariant System with Unknown Input
203(2)
Time Varying System with Measurable Input-Output
205(1)
Time Varying System with Unknown Input
206(1)
Exercises
207(2)
Modeling the Nerve Action Potential
209(26)
Electrical Behavior of Excitable Tissue
209(7)
Excitation of Nerves: The Action Potential
210(1)
Extracellular and Intracellular Compartments
211(1)
Membrane Potentials
211(2)
Electrical Equivalent of the Nerve Membrane
213(3)
The Voltage Clamp Experiment
216(4)
Opening the Feedback Loop of the Membrane
217(1)
Results of the Hodgkin-Huxley Experiments
218(2)
Interpreting the Voltage-Clamp Experimental Data
220(8)
Step Responses of the Ionic Conductances
220(1)
Hodgkin and Huxley's Nonlinear Model
221(4)
The Voltage Dependent Membrane Constants
225(1)
Simulation of the Hodgkin-Huxley Model
226(2)
A Model for the Strength-Duration Curve
228(7)
Exercises
230(1)
Programming Exercise
231(4)
Modeling Skeletal Muscle Contraction
235(32)
Skeletal Muscle Contraction
235(1)
Properties of Skeletal Muscle
236(7)
Isometric Properties of Skeletal Muscle
237(3)
The Sliding Filament Hypothesis
240(1)
The Sarcomere as the Unit of Muscle Contraction
241(2)
The Cross-Bridge Theory of Muscle Contraction
243(12)
The Molecular Force Generator
245(2)
Isotonic Experiments and the Force-Velocity curve
247(3)
Huxley's Model of Isotonic Muscle Contraction
250(5)
A Linear Model of Muscle Contraction
255(5)
Linear Approximation of the Force-Velocity Curve
255(1)
A Mechanical Analogue Model for Muscle
255(5)
Applications of Skeletal Muscle Modeling
260(7)
A Model of Intrafusal Muscle Fibers
260(2)
Other Applications of Muscle Modeling
262(1)
Exercises
263(1)
Programming Exercise
264(3)
Modeling Myoelectric Activity
267(28)
Electromyography
267(5)
Functional Organization of Skeletal Muscle
268(1)
Recording the EMG
268(4)
A Model of the Electromyogram
272(23)
Bipolar Recording Filter Function
275(4)
The Motor Unit
279(4)
The Interference EMG
283(6)
Exercises
289(2)
Programming Exercise
291(4)
System Identification in Physiology
295(14)
Black Box Modeling of Physiological Systems
295(1)
Sensory Receptors
295(8)
Firing Rate-Demodulation of Frequency Coding
296(5)
Estimating Receptor Transfer Function
301(2)
Pupil Control System
303(6)
Opening the Loop
303(3)
Estimating the Loop Transfer Function
306(1)
Instability of the Pupil Controller
306(1)
Applications of System Identification in Physiology
307(1)
Exercises
307(2)
Modeling the Cardiovascular System
309(12)
The Circulatory System
309(9)
Modeling Blood Flow
311(1)
Electrical Analogue of Flow in Vessels
311(3)
Simple Model of Systemic Blood Flow
314(3)
Modeling Coronary Circulation
317(1)
Other Applications of Cardiovascular Modeling
318(3)
Exercises
319(2)
A Model of the Immune Response to Disease
321(8)
Behavior of the Immune System
321(2)
Linearized Model of the Immune Response
323(6)
System Equations for the Immune Response
325(1)
Stability of the System
326(1)
Extensions to the Model
327(1)
Exercises
328(1)
Appendix 329(2)
Bibliography 331(4)
Index 335