Signals and Systems Analysis in Biomedical Engineering

The first edition of this text, based on the author’s 30 years of teaching and research on neurosensory systems, helped biomedical engineering students and professionals strengthen their skills in the common network of applied mathematics that ties together the diverse disciplines that comprise this field. Updated and revised to include new material as the field has grown, Signals and Systems Analysis in Biomedical Engineering, Second Edition continues to provide a ready source of information on those specialized mathematical techniques most useful in describing and analyzing biomedical signals.
New chapters on nonlinear and complex systems
Enriched with many examples that promote sound practical analysis, this volume covers classical linear systems theory and its applications to biomedicine. It examines the important use of joint time-frequency analysis to characterize non-stationary physiological signals, and explores the mathematics of tomographic imaging (the Radon transform, the Fourier slice theorem, and the filtered back-projection algorithm). It also describes the analytical signal and the Hilbert transform and some of its biomedical applications. New chapters in this edition include one on the analysis of nonlinear biochemical systems and biochemical oscillators, as well as one introducing complex systems and illustrating ways to best model them.
Four appendices with additional material
Extensive appendices supplement the text, including "Simnon® Programs Used in Chapters 11 and 12," "How to use Root Locus to Determine the Stability of SISO Linear Systems," "Signal Flow Graphs and Mason’s Rule," and "Computational Tools for Biomedical Signal Processing and Systems Analysis." An extensive glossary is included as well as an ample listing of sources for further study.
A solutions manual is avaialble for instructors wishing to convert this refrence to classroom use.

Robert B. Northrop graduated with a bachelor’s degree in electrical engineering from the Massachusetts Institute of Technology in 1956. At the University of Connecticut (UCONN), he received a master’s degree in systems engineering in 1958. As the result of a long-standing interest in physiology, he entered a PhD program at UCONN in physiology, doing research on the neuromuscular physiology of molluskan catch muscles. He received his PhD in 1964. His current research interest lies in complex systems. Dr. Northrop was on the electrical and computer engineering faculty at UCONN until his retirement in June 1997. Throughout this time, he was director of the BME graduate program. As emeritus professor, he still teaches courses in BME, writes texts, sails, and travels. He lives in Chaplin, CT, with his wife, and a smooth fox terrier.

Introduction to Biomedical Signals and SystemsGeneral Characteristics of Biomedical Signals General Properties of PSs Review of Linear Systems TheoryLinearity, Causality, and StationarityAnalog SystemsSystems Described by Sets of ODEsLinear System CharacterizationDiscrete Signals and SystemsStability of SystemsThe Laplace Transform and Its ApplicationsIntroductionProperties of the Laplace TransformSome Examples of Finding Laplace TransformsThe Inverse Laplace TransformApplications of the Laplace TransformFourier Series Analysis of Periodic SignalsIntroductionProperties of the FSFS ExamplesThe Continuous Fourier TransformIntroductionProperties of the CFTADC and the Sampling TheoremThe Analytical Signal and the HTMTF in ImagingThe Discrete Fourier TransformIntroductionThe CFT, ICFT, DFT, and IDFTData Window FunctionsThe FFTIntroduction to Joint TimeFrequency Analysis of Biomedical SignalsIntroduction The Short-Term Fourier TransformThe Gabor and Adaptive Gabor TransformsThe WignerVille and PseudoWigner Transforms Cohen’s General Class of JTF Distributions Introduction to JTFA Using Wavelets Applications of JTFA to Physiological Signals JTFA Software Introduction to the Analysis of Stationary Noise, and Signals Contaminated with NoiseIntroductionNoise Descriptors and Noise in SystemsCalculation of Noise Descriptors with Finite Discrete DataSignal Averaging and Filtering for SNR ImprovementIntroduction to the Application of Statistics and IT to Genomics Basic Mathematical Tools Used in the Characterization of Physiological SystemsIntroductionSome General Properties of PSsSome Properties of Nonlinear SystemsPhysical Factors Determining the Dynamic Behavior of PSsMeans of Characterizing PSsIntroduction to the Mathematics of Tomographic ImagingIntroductionAlgebraic ReconstructionThe Radon TransformThe Fourier Slice TheoremFiltered BackProjection AlgorithmIntroduction to the Analysis of Nonlinear Biochemical Systems and Biochemical OscillatorsIntroduction: Some General Properties of Nonlinear Systems All Living Systems Are NonlinearParametric Regulation in Nonlinear Biological SystemsApproaches to Nonlinear Analysis: the Phase Plane Chaos, Stability, and Limit Cycles in Nonlinear Biological Systems Introduction to Complex Systems in Biology and MedicineIntroduction to Complex SystemsWhen Is a System Complex?Some ExamplesProperties of Complex Systems: Chaos and Tipping Points The Law of Unintended Consequences Why Study Complex Systems?Human Responses to ComplexityComplex Systems EngineeringSome Complex Physiological Regulatory SystemsStructure and Function: Some Examples of Complex Physiological Regulatory Systems and Their Simplified ModelsExamples of When Complex Physiological Systems FailSome Approaches to Dealing with Complexity in an Organized Manner Glossary Appendix AAppendix BAppendix CAppendix DIndex