Nonlinear Biomedical Signal Processing, Volume 2: Dynamic Analysis and Modeling (IEEE Press Series on Biomedical Enginee
Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of physiological systems. Detailed discussions of the analysis of steady-state and dynamic systems, discrete-time system theory, and discrete modeling of continuous-time systems are provided. Biomedical examples include the analysis of the respiratory control system, the dynamics of cardiac muscle and the cardiorespiratory function, and neural firing patterns in auditory and vision systems. Examples include relevant MATLAB® and Pascal programs.
Topics covered include:
- Nonlinear dynamics
- Behavior and estimation
- Modeling of biomedical signals and systems
- Heart rate variability measures, models, and signal assessments
- Origin of chaos in cardiovascular and gastric myoelectrical activity
- Measurement of spatio-temporal dynamics of human epileptic seizures
Why Read This Book
You should read this book if you need a focused introduction to nonlinear dynamic methods and models as applied to real biomedical signals — it ties mathematical techniques to physiological examples. You will get practical perspectives from multiple experts on detecting nonlinearity, reconstructing dynamics, and building discrete-time models for cardiac, respiratory, and neural data.
Who Will Benefit
Graduate students, researchers, and engineers in biomedical signal processing or clinical researchers who want to apply nonlinear time-series and modeling tools to ECG, EEG, respiratory and other physiological signals.
Level: Advanced — Prerequisites: Solid background in signals and systems, linear DSP, calculus and differential equations, basic probability/statistics; familiarity with MATLAB or similar computing environments is strongly recommended.
Key Takeaways
- Apply phase-space reconstruction and compute invariants such as Lyapunov exponents and fractal dimension on physiological time series.
- Detect and test for nonlinearity in biomedical signals using surrogate data and statistical hypothesis techniques.
- Construct and validate nonlinear (discrete-time) models of physiological systems including cardiac and respiratory control.
- Use nonlinear spectral and dynamical measures to characterize ECG/EEG and to detect dynamical transitions or pathology-related changes.
- Implement computational workflows for nonlinear time-series analysis and understand practical limitations when data are noisy and short.
- Interpret results of nonlinear analyses in the physiological/clinical context and combine them with statistical approaches.
Topics Covered
- Introduction: Nonlinear approaches in biomedical signal processing
- Foundations of nonlinear dynamics and chaos for time-series analysis
- Phase-space reconstruction and embedding techniques
- Quantitative invariants: Lyapunov exponents, entropy, and fractal dimension
- Surrogate data methods and statistical testing for nonlinearity
- Nonlinear system identification and discrete-time modeling of physiological systems
- Modeling of cardiac dynamics and cardiac muscle behavior
- Analysis and modeling of the respiratory control system
- Nonlinear analysis of neural signals and EEG/ERP applications
- Nonlinear filtering, estimation, and signal reconstruction
- Numerical methods, simulation, and algorithmic implementation
- Case studies: applications to clinical and experimental biomedical data
- Conclusions, limitations, and future directions
Languages, Platforms & Tools
How It Compares
Complementary to Kantz & Schreiber's "Nonlinear Time Series Analysis" (which is stronger on core theory and algorithms), Akay's volume focuses more on biomedical applications and modeling; Volume I of Akay's series covers additional methods and image-processing-oriented topics.
