Signals and systems in biomedical engineering: signal processing and physiological systems modeling
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York [u.a.]
Kluwer Acad., Plenum Publ.
2000
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| Series: | Topics in biomedical engineering international book series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVI, 337 S. graph. Darst. 1 CD-Rom ; 12 cm |
| ISBN: | 0306463911 |
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MARC
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| 245 | 1 | 0 | |a Signals and systems in biomedical engineering |b signal processing and physiological systems modeling |c Suresh R. Devasahayam |
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Record in the Search Index
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| adam_text | Contents
1. Introduction to Systems Analysis and Numerical Methods 1
1.1. The Systems Approach to Physiological Analysis 1
1.1.1. Physiological Signals and Systems 2
1.1.2. Linear Systems Modeling in Physiology 3
1.2. Numerical Methods for Data Analysis and Simulation 3
1.2.1. Numerical Integration and Differentiation 4
1.2.2. Graphical Display 7
1.3. Examplesof Physiological Models 8
EXERCISES 9
2. Continuous Time Signals and Systems 11
2.1. Physiological Measurement and Analysis 11
2.2. Time Signals 12
2.2.1. Examples of Physiological Signals 12
2.2.2. Operations on Time Signals 13
2.3. input Output Systems 16
2.3.1. Properties of Systems 16
2.3.2. Linear Time Invariant Systems 18
2.3.3. Impulse Response of a Linear Time Invariant System 20
2.3.4. The Convolution Integral 21
2.3.5. Properties of Convolution 22
EXERCISES 30
3. Fourier Analysis for Continuous Time Processes 31
3.1. DECOMPOSITION OF PERIODIC SIGNALS 31
xi
xii Contents
3.1.1. Synthesis of an ECG Signal Using Pure Sinusoids 32
3.2. FOURIER CONVERSIONS 36
3.2.1. Periodic Continuous Time Signals: Fourier Series 36
3.2.2. Aperiodic Continuous Time Signals: Fourier Transform 42
3.2.3. Properties of the Fourier Domain 44
3.3. System Transfer Function 46
3.3.1. The Laplace Transform 47
3.3.2. Properties of the Laplace Transform 47
3.3.3. Frequency Response of LTI Systems 52
3.3.4. Pole Zero Plots and Bode Plots 53
3.3.5. Frequency Filters 62
3.3.6. Phase Shifts and Time Delays 64
3.4. Systems Representation of Physiological Processes 64
EXERCISES 65
4. Discrete Time Signals and Systems 67
4.1. DlSCRETIZATION OF CONTINUOUS TlME SIGNALS 67
4.1.1. Sampling and Quantization 68
4.1.2. The Sampling Theorem 69
4.1.3. Reconstruction of a Signal from Its Sampled Version 73
4.1.4. Quantization of Sampled Data 74
4.1.5. Data Conversion Time Sample and Hold 75
4.2. DlSCRETE TlME SIGNALS 77
4.2.1. Analogue to Digital Conversion 77
4.2.2. Operations on Discrete Time Signals 77
4.3. DlSCRETE TlME SYSTEMS 78
4.3.1. The Impulse Response of a Discrete LTI System 79
4.3.2. The Convolution Sum 79
4.3.3. Properties of the Discrete Convolution Operation 80
4.3.4. Examples of the Convolution Sum 80
4.3.5. Frequency Filtering by Discrete Time Systems 81
4.3.6. Determination of Impulse Response from I/O Relation 86
4.4. Random Signals 87
4.4.1. Statistical Descriptions of Random Signals 91
4.4.2. Ensemble Average and Time Average 92
4.4.3. Stationary Processes 94 i
4.4.4. Auto correlation and Cross Correlation of Discrete Signals.. 95
EXERCISES 97
PROGRAMMING EXERCISE 98
Contents xiii
5. Fourier Analysis for Discrete Time Processes 101
5.1. Discrete Fourier Conversions 101
5.1.1. Periodic Discrete Time Signals: Discrete Fourier Series 101
5.1.2. Aperiodic Discrete Time Signals: DTFT 102
5.1.3. Numerical Implementation of Fourier Conversion: DFT 106
5.1.4. Inter Relations among Fourier Conversions 109
5.2. Applying the Discrete Fourier Transform 112
5.2.1. Properties of the DFT 112
5.2.2. Windowing 115
5.2.3. The Fast Fourier Transform 117
5.2.4. Convolution Using the FFT Circular Convolution 120
5.3. The Z Transform 122
5.3.1. Properties of the Z transform 122
5.3.2. The Bilinear Transformation 125
5.4. Discrete Fourier Transform of Random Signals 127
5.4.1. Estimating the Power Spectrum 127
5.4.2. Transfer Function Estimation or System Identification 129
EXERCISES 130
PROGRAMMING EXERCISES 132
6. Time Frequency and Wavelet Analysis 139
6.1. Time Varying Processes 139
6.2. The Short Time Fourier Transform 140
6.2.1. The Continuous Time STFT and the Gabor Transform 142
6.3. Wavelet Decomposition of Signals 143
6.3.1. Multi Resolution Decomposition 144
6.3.2. Hierarchical Filter Bank for Wavelet Decomposition 145
6.3.3. The Daubechies 4 Coefficient Wavelet Filters 147
6.4. The Wavelet Transform 152
6.4.1. Interpretation of the Wavelet Transform 157
6.4.2. The Inverse Wavelet Transform 158
6.5. Comparison of Fourier and Wavelet Transforms 161
EXERCISES 164
7. Estimation of Signals in Noise 165
7.1. Noise Reduction by Filtering 165
7.1.1. Mean Square Error Minimization 166
7.1.2. Optimal Filtering 169
7.2. Time Series Analysis 172
7.2.1. Systems with Unknown Inputs Autoregressive Model 173
7.2.2. Time Series Model Estimation 174
xiv Contents
7.2.3. Recursive Identification of a Non Stationary Model 177
7.2.4. Time Series Modeling and Estimation in Physiology 179
EXERCISES 179
8. Feedback Systems 181
8.1. Physiological Systems with Feedback 181
8.2. Analysis of Feedback Systems 183
8.2.1. Advantages of Feedback Control 184
8.2.2. Analysis of Closed Loop System Stability using Bode Plots 189
8.3. Digital Control in Feedback Systems 193
EXERCISES 195
9. Model Based Analysis of Physiological Signals 197
9.1. Modeling Physiological Systems 197
9.1.1. Biophysical Models and Black Box Models 197
9.1.2. Purpose of Physiological Modeling and Signal Analysis 198
9.1.3. Linearization of Nonlinear Models 198
9.1.4. Validation of Model Behavior against Experiment 200
9.2. Model Based Noise Reduction and Feature Extraction . 200
9.2.1. Time Invariant System with Measurable Input Output 201
9.2.2. Time Invariant System with Unknown Input 203
9.2.3. Time Varying System with Measurable Input Output 205
9.2.4. Time Varying System with Unknown Input 206
EXERCISES 207
10. Modeling the Nerve Action Potential 209
10.1. Electrical Behavior of Excitable Tissue 209
10.1.1. Excitation of Nerves: The Action Potential 210
10.1.2. Extracellular and Intracellular Compartments 211
10.1.3. Membrane Potentials 211
10.1.4. Electrical Equivalent of the Nerve Membrane 213
10.2. The Voltage Clamp Experiment 216
10.2.1. Opening the Feedback Loop of the Membrane 217
10.2.2. Results of the Hodgkin Huxley Experiments 218
10.3. Interpreting the Voltage Clamp Experimental Data ... 220
10.3.1. Step Responses of the Ionic Conductances 220
10.3.2. Hodgkin and Huxley s Nonlinear Model 221
10.3.3. The Voltage Dependent Membrane Constants 225
10.3.4. Simulation of the Hodgkin Huxley Model 226
10.4. A Model for the Strength Duration Curve 228
EXERCISES 230
Contents xv
PROGRAMMING EXERCISE 231
11. Modeling Skeletal Muscle Contraction 235
11.1. Skeletal Muscle Contraction 235
11.2. Properties of Skeletal Muscle 236
11.2.1. Isometric Properties of Skeletal Muscle 237
11.2.2. The Sliding Filament Hypothesis 240
11.2.3. The Sarcomere as the Unit of Muscle Contraction 241
11.3. The Cross Bridge Theory of Muscle Contraction 243
11.3.1. The Molecular Force Generator 245
11.3.2. Isotonic Experiments and the Force Velocity curve 247
11.3.3. Huxley s Model of Isotonic Muscle Contraction 250
11.4. A Linear Model of Muscle Contraction 255
11.4.1. Linear Approximation of the Force Velocity Curve 255
11.4.2. A Mechanical Analogue Model for Muscle 255
11.5. Applications of Skeletal Muscle Modeling 260
11.5.1. A Model of Intrafusal Muscle Fibers 260
11.5.2. Other Applications of Muscle Modeling 262
EXERCISES 263
PROGRAMMING EXERCISE 264
12. Modeling Myoelectric Activity 267
12.1. Electromyography 267
12.1.1. Functional Organization of Skeletal Muscle 268
12.1.2. Recording the EMG 268
12.2. A Model of the Electromyogram 272
12.2.1. Bipolar Recording Filter Function 275
12.2.2. The Motor Unit 279
12.2.3. The Interference EMG 283
EXERCISES 289
PROGRAMMING EXERCISE 291
13. System Identification in Physiology 295
13.1. Black Box Modeling of Physiological Systems 295
13.2. Sensory Receptors 295
13.2.1. Firing Rate Demodulation of Frequency Coding 296
13.2.2. Estimating Receptor Transfer Function 301
13.3. Pupil Control System 303
13.3.1. Opening the Loop 303
13.3.2. Estimating the Loop Transfer Function 306
13.3.3. Instability of the Pupil Controller 306
xv i Contents
13.4. Applications of System Identification in Physiology .... 307
EXERCISES 307
14. Modeling the Cardiovascular System 309
14.1. The Circulatory System 309
14.1.1. Modeling Blood Flow 311
14.1.2. Electrical Analogue of Flow in Vessels 311
14.1.3. Simple Model of Systemic Blood Flow 314
14.1.4. Modeling Coronary Circulation 317
14.2. Other Applications of Cardiovascular Modeling 318
EXERCISES 319
15. A Model of the Immune Response to Disease 321
15.1. Behavior of the Immune System 321
15.2. Linearized Model of the Immune Response 323
15.2.1. System Equations for the Immune Response 325
15.2.2. Stability of the System 326
15.2.3. Extensions to the Model 327
EXERCISES 328
APPENDIX 329
BIBLIOGRAPHY 331
Index 335
|
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| spelling | Devasahayam, Suresh R. Verfasser aut Signals and systems in biomedical engineering signal processing and physiological systems modeling Suresh R. Devasahayam New York [u.a.] Kluwer Acad., Plenum Publ. 2000 XVI, 337 S. graph. Darst. 1 CD-Rom ; 12 cm txt rdacontent n rdamedia nc rdacarrier Topics in biomedical engineering international book series Biotechnologie ram Physiologie - Modèles mathématiques ram Traitement du signal ram Mathematisches Modell Biomedical Engineering Biomedical engineering Models, Biological Models, Theoretical Physiology Mathematical models Signal Processing, Computer-Assisted Signal processing Biosignalverarbeitung (DE-588)4006899-7 gnd rswk-swf Biosignalverarbeitung (DE-588)4006899-7 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009872033&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Devasahayam, Suresh R. Signals and systems in biomedical engineering signal processing and physiological systems modeling Biotechnologie ram Physiologie - Modèles mathématiques ram Traitement du signal ram Mathematisches Modell Biomedical Engineering Biomedical engineering Models, Biological Models, Theoretical Physiology Mathematical models Signal Processing, Computer-Assisted Signal processing Biosignalverarbeitung (DE-588)4006899-7 gnd |
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| title | Signals and systems in biomedical engineering signal processing and physiological systems modeling |
| title_auth | Signals and systems in biomedical engineering signal processing and physiological systems modeling |
| title_exact_search | Signals and systems in biomedical engineering signal processing and physiological systems modeling |
| title_full | Signals and systems in biomedical engineering signal processing and physiological systems modeling Suresh R. Devasahayam |
| title_fullStr | Signals and systems in biomedical engineering signal processing and physiological systems modeling Suresh R. Devasahayam |
| title_full_unstemmed | Signals and systems in biomedical engineering signal processing and physiological systems modeling Suresh R. Devasahayam |
| title_short | Signals and systems in biomedical engineering |
| title_sort | signals and systems in biomedical engineering signal processing and physiological systems modeling |
| title_sub | signal processing and physiological systems modeling |
| topic | Biotechnologie ram Physiologie - Modèles mathématiques ram Traitement du signal ram Mathematisches Modell Biomedical Engineering Biomedical engineering Models, Biological Models, Theoretical Physiology Mathematical models Signal Processing, Computer-Assisted Signal processing Biosignalverarbeitung (DE-588)4006899-7 gnd |
| topic_facet | Biotechnologie Physiologie - Modèles mathématiques Traitement du signal Mathematisches Modell Biomedical Engineering Biomedical engineering Models, Biological Models, Theoretical Physiology Mathematical models Signal Processing, Computer-Assisted Signal processing Biosignalverarbeitung |
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