Biomedical Signal Processing and Signal Modeling
A biomedical engineering perspective on the theory, methods, and applications of signal processing This book provides a unique framework for understanding signal processing of biomedical signals and what it tells us about signal sources and their behavior in response to perturbation. Using a modeling-based approach, the author shows how to perform signal processing by developing and manipulating a model of the signal source, providing a logical, coherent basis for recognizing signal types and for tackling the special challenges posed by biomedical signals-including the effects of noise on the signal, changes in basic properties, or the fact that these signals contain large stochastic components and may even be fractal or chaotic. Each chapter begins with a detailed biomedical example, illustrating the methods under discussion and highlighting the interconnection between the theoretical concepts and applications. The author has enlisted experts from numerous subspecialties in biomedical engineering to help develop these examples and has made most examples available as Matlab or Simulink files via anonymous ftp. Without the need for a background in electrical engineering, readers will become acquainted with proven techniques for analyzing biomedical signals and learn how to choose the appropriate method for a given application. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the author.
Why Read This Book
You should read this book if you want a coherent, model-driven treatment of how DSP techniques reveal physiology from noisy biomedical measurements. It teaches you to build and manipulate signal-source models so you can apply spectral, parametric, and nonlinear methods with a clear interpretation for ECG, EEG, EMG and related signals.
Who Will Benefit
Graduate students and practicing engineers working on biomedical signal analysis or algorithm development who need a bridge between DSP methods and physiological interpretation.
Level: Intermediate — Prerequisites: Basic signals and systems, linear algebra, and probability/statistics; familiarity with core DSP concepts (Fourier transforms, filtering) and MATLAB is recommended.
Key Takeaways
- Model biomedical signals using deterministic and stochastic frameworks (AR/ARMA, state-space) to capture physiological sources.
- Apply parametric and nonparametric spectral estimation techniques to noisy, often nonstationary biomedical data.
- Design and evaluate filters and detection/estimation algorithms tailored to ECG, EEG and other biosignals.
- Analyze nonlinear and fractal behaviors in physiological signals and use appropriate measures to characterize them.
- Use system-identification approaches to infer source dynamics and the effect of perturbations on recorded signals.
Topics Covered
- 1. Introduction and Biomedical Examples
- 2. Review of Signal Processing Foundations for Biomedical Signals
- 3. Deterministic Signal Modeling and Parameter Estimation
- 4. Stochastic Models for Biomedical Signals (AR/ARMA, State-Space)
- 5. Spectral Analysis: Nonparametric and Parametric Methods
- 6. Time-Varying and Nonstationary Signal Analysis
- 7. Detection and Estimation in Biomedical Contexts
- 8. System Identification and Source Modeling
- 9. Nonlinear Dynamics, Fractals and Chaos in Physiological Signals
- 10. Practical Issues: Noise, Preprocessing, and Artifact Handling
- 11. Case Studies: ECG, EEG, EMG and Other Applications
- 12. Implementation Considerations and MATLAB Examples
Languages, Platforms & Tools
How It Compares
Similar in audience to Rangayyan's Biomedical Signal Analysis but Bruce emphasizes a unified, model-based statistical/DSP framework and nonlinear dynamics more than signal-feature extraction and biomedical imaging.
