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
About the Author:
Suresh Devasahayam has taught students of science, technology, engineering, and medicine for more than 25 years, first at the Indian Institute of Technology, Bombay, for about 12 years and then at the Christian Medical College, Vellore.
In teaching such a variety of students, it has been important to span a wide range of language skills - from formal mathematical language for students of the physical sciences to natural language for students of the biological sciences. These two extremes of communication in science also represent the difference between theoretical argument from first principles and empirical knowledge; therefore, his research and teaching has included both theoretical analysis and physiological experiments. He has greatly enjoyed the extension of language by the use of dynamic graphs and animations through computer simulations.
His formal training includes a Baccalaureate in Electronics and Communications Engineering from the College of Engineering, Guindy, followed by Master's and Doctoral degrees in Bioengineering from the University of Illinois at Chicago. His research is in the areas of physiological measurement, medical instrumentation, signal processing, systems modeling, and neurorehabilitation.
