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Modeling & Imaging of Bioelectrical Activity: Principles and Applications 2005 ed. [Kietas viršelis]

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  • Formatas: Hardback, 322 pages, aukštis x plotis: 250x170 mm, weight: 853 g, 203 Illustrations, black and white; XIV, 322 p. 203 illus., 1 Hardback
  • Serija: Bioelectric Engineering
  • Išleidimo metai: 30-Apr-2004
  • Leidėjas: Kluwer Academic/Plenum Publishers
  • ISBN-10: 030648112X
  • ISBN-13: 9780306481123
Kitos knygos pagal šią temą:
Modeling & Imaging of Bioelectrical Activity: Principles and Applications 2005 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 322 pages, aukštis x plotis: 250x170 mm, weight: 853 g, 203 Illustrations, black and white; XIV, 322 p. 203 illus., 1 Hardback
  • Serija: Bioelectric Engineering
  • Išleidimo metai: 30-Apr-2004
  • Leidėjas: Kluwer Academic/Plenum Publishers
  • ISBN-10: 030648112X
  • ISBN-13: 9780306481123
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780306481123.html
Raktažodžiai:
Over the past several decades, much progress has been made in understanding the mechanisms of electrical activity in biological tissues and systems, and for developing non-invasive functional imaging technologies to aid clinical diagnosis of dysfunction in the human body. The book will provide full basic coverage of the fundamentals of modeling of electrical activity in various human organs, such as heart and brain. It will include details of bioelectromagnetic measurements and source imaging technologies, as well as biomedical applications. The book will review the latest trends in the field and comment on the future direction in this fast developing line of research.
1 FROM CELLULAR ELECTROPHYSIOLOGY TO ELECTROCARDIOGRAPHY 1(42)
Nitish V. Thakor, Vivek Iyer, and Mahesh B. Shenai
1 Introduction
1(42)
1.1 The One-cell Model
3(14)
1.1.1 Voltage Gating Ion Channel Kinetics (Hodgkin-Huxley Formalism)
3(4)
1.1.2 Modeling the Cardiac Action Potential
7(3)
1.1.3 Modeling Pathologic Action Potentials
10(7)
1.2 Network Models
17(12)
1.2.1 Cell-cell Coupling and Linear Cable Theory
17(1)
1.2.2 Multidimensional Networks
18(2)
1.2.3 Reconstruction of the Local Extracellular Electrogram (Forward Problem)
20(3)
1.2.4 Modeling Pathology in Cellular Networks
23(6)
1.3 Modeling Pathology in Three-dimensional and Whole Heart Models
29(7)
1.3.1 Myocardial Ischemia
31(1)
1.3.2 Preexcitation Studies
31(3)
1.3.3 Hypertrophic Cardiomyopathy
34(1)
1.3.4 Drug Integration in Three-dimensional Whole Heart Models
35(1)
1.3.5 Genetic Integration in Three-dimensional Whole Heart Models
35(1)
1.4 Discussion
36(2)
References
38(5)
2 THE FORWARD PROBLEM OF ELECTROCARDIOGRAPHY: THEORETICAL UNDERPINNINGS AND APPLICATIONS 43(38)
Ramesh M. Gulrajani
2.1 Introduction
43(1)
2.2 Dipole Source Representations
44(1)
2.2.1 Fundamental Equations
44(2)
2.2.2 The Bidomain Myocardium
46(7)
2.3 Torso Geometry Representations
53(1)
2.4 Solution Methodologies for the Forward problem
53(1)
2.4.1 Surface Methods
54(4)
2.4.2 Volume Methods
58(3)
2.4.3 Combination Methods
61(1)
2.5 Applications of the Forward Problem
61(1)
2.5.1 A Computer Heart Models
62(8)
2.5.2 Effects of Torso Conductivity Inhomogeneities
70(2)
2.5.3 Defibrillation
72(3)
2.6 Future Trends
75(1)
References
75(6)
3 WHOLE HEART MODELING AND COMPUTER SIMULATION 81(38)
Darning Wei
3.1 Introduction
81(1)
3.2 Methodology in 3D Whole Heart Modeling
82(1)
3.2.1 Heart-torso Geometry Modeling
82(1)
3.2.2 Inclusion of Specialized Conduction System
83(2)
3.2.3 Incorporating Rotating Fiber Directions
85(4)
3.2.4 Action Potentials and Electrophysiologic Properties
89(5)
3.2.5 Propagation Models
94(6)
3.2.6 Cardiac Electric Sources and Surface ECG Potentials
100(3)
3.3 Computer Simulations and Applications
103(1)
3.3.1 Simulation of the Normal Electrocardiogram
103(4)
3.3.2 Simulation of ST-T Waves in Pathologic Conditions
107(1)
3.3.3 Simulation of Myocardial Infarction
108(2)
3.3.4 Simulation of Pace Mapping
110(1)
3.3.5 Spiral Waves-A New Hypothesis of Ventricular Fibrillation
110(1)
3.3.6 Simulation of Antiarrhythmic Drug Effect
110(1)
3.4 Discussion
111(3)
References
114(5)
4 HEART SURFACE ELECTROCARDIOGRAPHIC INVERSE SOLUTIONS 119(42)
Fred Greensite
4.1 Introduction
119(1)
4.1.1 The Rationale for Imaging Cardiac Electrical Function
120(1)
4.1.2 A Historical Perspective
120(3)
4.1.3 Notation and Conventions
123(1)
4.2 The Basic Model and Source Formulations
123(5)
4.3 Heart Surface Inverse Problems Methodology
128(1)
4.3.1 Solution Nonuniqueness and Instability
129(3)
4.3.2 Linear Estimation and Regularization
132(3)
4.3.3 Stochastic Processes and Time Series of Inverse Problems
135(3)
4.4 Epicardial Potential Imaging
138(1)
4.4.1 Statistical Regularization
138(1)
4.4.2 Tikhonov Regularization and Its Modifications
139(2)
4.4.3 Truncation Schemes
141(1)
4.4.4 Specific Constraints in Regularization
142(1)
4.4.5 Nonlinear Regularization Methodology
143(1)
4.4.6 An Augmented Source Formulation
143(1)
4.4.7 Different Methods for Regularization Parameter Selection
143(1)
4.4.8 The Body Surface Laplacian Approach
144(1)
4.4.9 Spatiotemporal Regularization
145(1)
4.4.10 Recent in Vitro and in Vivo Work
146(1)
4.5 Endocardial Potential Imaging
147(2)
4.6 Imaging Features of the Action Potential
149(1)
4.6.1 Myocardial Activation Imaging
149(5)
4.6.2 Imaging Other Features of the Action Potential
154(1)
4.7 Discussion
155(1)
References
156(5)
5 THREE-DIMENSIONAL ELECTROCARDIOGRAPHIC TOMOGRAPHIC IMAGING 161(22)
Bin He
5.1 Introduction
161(2)
5.2 Three-Dimensional Myocardial Dipole Source Imaging
163(1)
5.2.1 Equivalent Moving Dipole Model
163(1)
5.2.2 Equivalent Dipole Distribution Model
163(1)
5.2.3 Inverse Estimation of 3D Dipole Distribution
164(1)
5.2.4 Numerical Example of 3D Myocardial Dipole Source Imaging
165(2)
5.3 Three-Dimensional Myocardial Activation Imaging
167(1)
5.3.1 Outline of the Heart-Model based 3D Activation Time Imaging Approach
167(1)
5.3.2 Computer Heart Excitation Model
168(1)
5.3.3 Preliminary Classification System
169(1)
5.3.4 Nonlinear Optimization System
170(1)
5.3.5 Computer Simulation
171(3)
5.3.6 Discussion
174(1)
5.4 Three-Dimensional Myocardial Transmembrane Potential Imaging
175(3)
5.5 Discussion
178(2)
References
180(3)
6 BODY SURFACE LAPLACIAN MAPPING OF BIOELECTRIC SOURCES 183(30)
Bin He and Jie Lian
6.1 Introduction
183(1)
6.1.1 High-resolution ECG and EEG
183(1)
6.1.2 Biophysical Background of the Surface Laplacian
184(2)
6.2 Surface Laplacian Estimation Techniques
186(1)
6.2.1 Local Laplacian Estimates
186(2)
6.2.2 Global Laplacian Estimates
188(2)
6.2.3 Surface Laplacian Based Inverse Problem
190(2)
6.3 Surface Laplacian Imaging of Heart Electrical Activity
192(1)
6.3.1 High-resolution Laplacian ECG Mapping
192(1)
6.3.2 Performance Evaluation of the Spline Laplacian ECG
193(6)
6.3.3 Surface Laplacian Based Epicardial Inverse Problem
199(1)
6.4 Surface Laplacian Imaging of Brain Electrical Activity
200(1)
6.4.1 High-resolution Laplacian EEG Mapping
200(1)
6.4.2 Performance Evaluation of the Spline Laplacian EEG
200(6)
6.4.3 Surface Laplacian Based Cortical Imaging
206(2)
6.5 Discussion
208(1)
References
209(4)
7 NEUROMAGNETIC SOURCE RECONSTRUCTION AND INVERSE MODELING 213(38)
Kensuke Sekihara and Srikantan S. Nagarajan
7.1 Introduction
213(1)
7.2 Brief Summary of Neuromagnetometer Hardware
214(1)
7.3 Forward Modeling
215(1)
7.3.1 Definitions
215(1)
7.3.2 Estimation of the Sensor Lead Field
216(3)
7.3.3 Low-rank Signals and Their Properties
219(2)
7.4 Spatial Filter Formulation and Non-adaptive Spatial Filter Techniques
221(1)
7.4.1 Spatial Filter Formulation
221(1)
7.4.2 Resolution Kernel
222(1)
7.4.3 Non-adaptive Spatial Filter
222(3)
7.4.4 Noise Gain and Weight Normalization
225(1)
7.5 Adaptive Spatial Filter Techniques
226(1)
7.5.1 Scalar Minimum-variance-based Beamformer Techniques
226(1)
7.5.2 Extension to Eigenspace-projection Beamformer
227(1)
7.5.3 Comparison between Minimum-variance and Eigenspace Beamformer Techniques
228(2)
7.5.4 Vector-type Adaptive Spatial Filter
230(2)
7.6 Numerical Experiments: Resolution Kernel Comparison between Adaptive and Non-adaptive Spatial Filters
232(1)
7.6.1 Resolution Kernel for the Minimum-norm Spatial Filter
232(2)
7.6.2 Resolution Kernel for the Minimum-variance Adaptive Spatial Filter
234(1)
7.7 Numerical Experiments: Evaluation of Adaptive Beamformer Performance
235(1)
7.7.1 Data Generation and Reconstruction Condition
235(3)
7.7.2 Results from Minimum-variance Vector Beamformer
238(1)
7.7.3 Results from the Vector-extended Borgiotti-Kaplan Beamformer
238(1)
7.7.4 Results from the Eigenspace Projected Vector-extended Borgiotti-Kaplan Beamformer
238(5)
7.8 Application of Adaptive Spatial Filter Technique to MEG Data
243(1)
7.8.1 Application to Auditory-somatosensory Combined Response
243(2)
7.8.2 Application to Somatosensory Response: High-resolution Imaging Experiments
245(2)
References
247(4)
8 MULTIMODAL IMAGING FROM NEUROELECTROMAGNETIC AND FUNCTIONAL MAGNETIC RESONANCE RECORDINGS 251(30)
Fabio Babiloni and Febo Cincotti
8.1 Introduction
251(1)
8.2 Generalities on Functional Magnetic Resonance Imaging
252(2)
8.2.1 Block-design and Event-Related fMRI
254(1)
8.3 Inverse Techniques
254(1)
8.3.1 Acquisition of Volume Conductor Geometry
255(1)
8.3.2 Dipole Localization Techniques
256(1)
8.3.3 Cortical Imaging
257(2)
8.3.4 Distributed Linear Inverse Estimation
259(2)
8.4 Multimodal Integration of EEG, MEG and fMRI Data
261(1)
8.4.1 Visible and Invisible Sources
261(1)
8.4.2 Experimental Design and Co-registration Issues
262(1)
8.4.3 Integration of EEG and MEG Data
263(4)
8.4.4 Functional Hemodynamic Coupling and Inverse Estimation of Source Activity
267(8)
8.5 Discussion
275(1)
References
276(5)
9 THE ELECTRICAL CONDUCTIVITY OF LIVING TISSUE: A PARAMETER IN THE BIOELECTRICAL INVERSE PROBLEM 281(40)
Maria J. Peters, Jeroen G. Stinstra, and Ibolya Leveles
9.1 Introduction
281(1)
9.1.1 Scope of this
Chapter
282(1)
9.1.2 Ambiguity of the Effective Conductivity
283(1)
9.1.3 Measuring the Effective Conductivity
284(3)
9.1.4 Temperature Dependence
287(1)
9.1.5 Frequency Dependence
287(2)
9.2 Models of Human Tissue
289(1)
9.2.1 Composites of Human Tissue
289(3)
9.2.2 Conductivities of Composites of Human Tissue
292(4)
9.2.3 Maxwell's Mixture Equation
296(4)
9.2.4 Archie's Law
300(7)
9.3 Layered Structures
307(1)
9.3.1 The Scalp
307(1)
9.3.2 The Skull
308(2)
9.3.3 A Layer of Skeletal Muscle
310(1)
9.4 Compartments
311(1)
9.4.1 Using Implanted Electrodes
311(1)
9.4.2 Combining Measurements of the Potential and the Magnetic Field
312(1)
9.4.3 Estimation of the Equivalent Conductivity using Impedance Tomography
312(1)
9.5 Upper and Lower Bounds
313(1)
9.5.1 White Matter
314(1)
9.5.2 The Fetus
314(2)
9.6 Discussion
316(1)
References
316(5)
INDEX 321


Bin He, PhD., is a leading figure in the field of bioelectric engineering. An internationally recognized scientist with numerous publications, Dr. He has served as the President of the International Society of Bioelectromagnetism and as an Associate or Guest Editor for nine international journals in the field of biomedical engineering. Dr. Bin He is currently Professor of Bioengineering at the University of Minnesota.