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" Signals and systems in biomedical engineering : "
Suresh R. Devasahayam.
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BL
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891321
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Devasahayam, Suresh R.
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| Title & Author
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Signals and systems in biomedical engineering : : physiological systems modeling and signal processing /\ Suresh R. Devasahayam.
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| Edition Statement
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Third edition.
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| Publication Statement
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Singapore :: Springer,, 2019.
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| Page. NO
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1 online resource (xv, 468 pages) :: illustrations (some color)
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| ISBN
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9789811335310
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: 9789811335327
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: 9811335311
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: 981133532X
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9789811335303
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9811335303
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Module1-Introduction to Signal Measurement and Analysis in Physiology.- Chapter 1.1 Measurement, Analysis, Modelling and Simulation.- Chapter 1.2. Physiological Measurement -- ECG as an example.- Chapter 1.3. Sensors and Measurement.- Chapter 1.4. Characterizing Transducers -- A Systems Approach.- Chapter 1.5. Interference and Noise.- Chapter 1.6. Simulation of Systems and Virtual Experiments.- Chapter 1.7. Execises.- Module 2-Basics of Signals and Systems.- Chapter 2.1. Time Domain Signals and Systems.- Chapter 2.2. Linear Systems: Impulse Response.- Chapter 2.3. Frequency Decomposition of Signals.- Chapter 2.4. Frequency Response and Pole-Zero plots.- Chapter 2.5. Random Signals.- Chapter 2.6. Exercises.- Module 3-Signal Filtering and System Control for Physiology.- Chapter 3.1. Filters in Different Domains- Mechanical filters, particle filters, electrical filters.- Chapter 3.2. A Common Sense View of Optimal filtering.- Chapter 3.3. Formal Definition of Optimal Filtering.- Chapter 3.4. Standard Filters: LPF, HPF, BPF, BSF.- Chapter 3.5. Realization of Simple Filters, Ensemble Averaging.- Chapter 3.6. Filtering Physiological Signals.- Chapter 3.7. Feedback Control Systems.- Chapter 3.8. Exercises.- Module 4-Digitization and Discrete Systems.- Chapter 4.1. Digitization -- From the Physical World to Computers and Back Again.- Chapter 4.2. Sampling, Quantization and Reconstruction Methods.- Chapter 4.3. Discrete Systems -- Z transforms.- Chapter 4.4. Discretization of Systems -- Bilinear transforms.- Chapter 4.5. Digital Feedback Control and Hybrid Systems.- Chapter 4.6.Exercises.- Module 5-Discrete Signal Processing.- Chapter 5.1. Digital Filtering and Sytem Identification.- Chapter 5.2. Discrete Fourier Transforms.- Chapter 5.3. Power Spectrum and Short-Time Fourier Transform.- Chapter 5.4. The Wavelet Transform.- Chapter 5.5. Time-Series Analysis.- Chapter 5.6. Programming Exercises.- Module 6-Numerical Methods, Graphics and Haptics for Modeling.- Chapter 6.1. Introduction to Computer Simulations.- Chapter 6.2. Geometry of 3D graphics.- Chapter 6.3. Animation and Image Manipulation.- Chapter 6.4. Virtual Experiments.- Chapter 6.5. Using electromechanical systems to provide 'feel' -- haptics.- Chapter 6.6. Basic haptics design.- Chapter 6.7. Exercises.- Module 7-Model-based Analysis of Physiological Systems.- Chapter 7.1. Biophysical Models and Black-Box Models.- Chapter 7.2. Purpose of Physiological Modelling and Signal Analysis.- Chapter 7.3. System identification in Physiology -- sensory receptors, eye movement.- Chapter 7.4. Opening the Loop -- Estimating Loop Transfer Function.- Chapter 7.5. Experimental Methods for System identification.- Chapter 7.6. Model-Based Noise Reduction and Feature Extraction.- Chapter 7.7. Exercises.- Module 8-Nerve Action Potential, Propagation and Stimulation of Tissue.- Chapter 8.1. Nerve Excitation and Propagation.- Chapter 8.2. The Hodgkin-Huxley Model, Fluctuation Analysis.- Chapter 8.3.Action Potential Propagation.- Chapter 8.4. Stimulation of Nerves within Tissue.- Chapter 8.5. Strength-Duration and Recruitment Relations.- Chapter 8.6. Electrical and Magnetic Stimulation.- Chapter 8.7. Exercises.- Module 9-Skeletal Muscle Contraction.- Chapter 9.1. Skeletal Muscle Behaviour, Structure and Organization.- Chapter 9.2. The Sliding Filament Model.- Chapter 9.3. Force Generation: Huxley's Model.- Chapter 9.4. Linearization of Skeletal Muscle Models.- Chapter 9.5. Simple haptics models of skeletal muscle as a non-linear spring.- Chapter 9.6. Applications of Muscle Modelling.- Chapter 9.7.Exercises.- Module 10-Neural Firing Analysis.- Chapter 10.1. Neural Information Transmission.- Chapter 10.2. Pulse sequences and Modulation Theory.- Chapter 10.3.Estimating Nerve Firing Rate.- Chapter 10.4. Spike Detection and Demodulation.- Chapter 10.5. Applications of Firing Rate Demodulation.- Chapter 10.6. Exercise.- Module 11-The Electromyogram -- Modelling and Analysis.- Chapter 11.1.Recording Myoelectric Signals.- Chapter 11.2. Electrode Transfer Function.- Chapter 11.3. Motor Unit Action Potential.- Chapter 11.4. Voluntary EMG Model.- Chapter 11.5. EMG Analysis.- Chapter 11.6. Hear, see, feel -- adding realism to EMG models.- Chapter 11.7. Exercises.- Module 12-Neuromuscular Control.- Chapter 12.1.Neuromuscular Reflex.- Chapter 12.2. Unit of Movement: Two Muscle Joint.- Chapter 12.3. Modelling Reflex Control of Movement.- Chapter 12.4. Movement Analysis.- Chapter 12.5. Understanding Pathology Using Neuromuscular Models.- Chapter 12.6. Incorporating haptics in neuromuscular models.- Chapter 12.7. Simulating spasticity -- what we can simulate is what we understand.- Chapter 12.8. Exercises.- Module 13-Cardiovascular Modelling.- Chapter 13.1. The Cardiovascular System.- Chapter 13.2. Modelling Blood Flow.- Chapter 13.3. Electrical Analogue of Fluid Flow in Vessels.- Chapter 13.4. Model of Coronary Circulation.- Chapter 13.5. Simulating the 'feel' of pulse auscultation.- Chapter 13.6.Applications of Cardiovascular Modelling.- Chapter 13.7. Exercises.-Module 14-Immune Response to Infection.- Chapter 14.1. The Immune Response.-Chapter 14.2. Linearized Model of the Immune Response.- Chapter 14.3. System Equations.- Chapter 14.4. Stability Analysis.- Chapter 14.5. Extending the Model.- Chapter 14.6. Exercises.
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| Abstract
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Physiology is a set of processes that maintain homeostasis, and physiological measurement is a means of observing these processes. Systems theory and signal processing offer formal tools for the study of processes and measured quantities. This book shows that systems modeling can be used to develop simulations of physiological systems, which use formal relations between the underlying processes and the observed measurements. The inverse of such relations suggest signal processing tools that can be applied to interpret experimental data. Both signal processing and systems modeling are invaluable in the study of human physiology. Discussing signal processing techniques ranging from filtering and spectrum analysis to wavelet analysis, the book also includesGraphs and analogies to supplement the mathematics and make the book more accessible to physiologists and also more interesting to engineers. Physiological systems modeling helps in both gaining insights and generating methods of analysis. This book shows how numerical computation with graphical display, haptics and multimedia can be used to simulate physiological systems. In this third edition the simulations are more closely related to clinical examination and experimental physiology than in previous editions. Detailed models of nerve and muscle at the cellular and systemic levels, and simplified models of cardiovascular blood flow provide examples for the mathematical methods and computer simulations. Several of the models are sufficiently sophisticated to be of value in understanding real world issues like neuromuscular disease. The book features expanded problem sets and a link to extra downloadable material, and simulation programs that are solutions to the theory developed in the text are also available.
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Biomedical engineering.
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Physiology-- Mathematical models.
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Signal processing.
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Biomedical engineering.
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Physiology-- Mathematical models.
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Signal processing.
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| Dewey Classification
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610.28
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| LC Classification
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R856.D48 2019eb
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