Synopses & Reviews
Synopsis
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 De
Synopsis
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 uses graphs and analogies to supplement the mathematics and make the book more accessible to physiologists and more interesting to engineers.
Physiological systems modeling helps in both gaining insight 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 containing simulation programs that are solutions to the theory developed in the text.