Neural Networks and Artificial Intelligence for Biomedical Engineering (IEEE Press Series on Biomedical Engineering)
Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems.
Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications.
Highlighted topics include:
- Types of neural networks and neural network algorithms
- Knowledge representation, knowledge acquisition, and reasoning methodologies
- Chaotic analysis of biomedical time series
- Genetic algorithms
- Probability-based systems and fuzzy systems
- Evaluation and validation of decision support aids
Why Read This Book
You should read this book if you want concrete examples and domain-specific guidance on applying neural networks, hybrid AI systems, and statistical pattern-recognition methods to biomedical signals and clinical decision support. It collects practical case studies and implementation approaches that connect AI techniques to real biomedical problems, making it a useful bridge between theory and applied biomedical signal analysis.
Who Will Benefit
Engineers and researchers in biomedical engineering or biomedical signal processing who need applied guidance on using neural networks, hybrid AI, and statistical methods to build diagnostic or decision-support systems.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, introductory probability/statistics, familiarity with signals (biosignals like ECG/EEG) and basic programming (MATLAB or similar). An introductory knowledge of neural networks is helpful but not strictly required.
Key Takeaways
- Understand the strengths and limitations of neural-network architectures applied to biomedical data and decision support.
- Apply supervised and unsupervised learning methods to clinical and biosignal classification tasks.
- Implement preprocessing and feature-extraction strategies tailored to common biomedical signals (e.g., ECG, EEG, imaging features).
- Integrate AI approaches with rule-based or hybrid systems to build practical decision-support applications.
- Evaluate and validate biomedical AI systems using appropriate statistical performance metrics and cross-validation strategies.
Topics Covered
- Introduction: AI and Neural Networks in Biomedical Engineering
- Foundations of Artificial Neural Networks
- Supervised Learning Algorithms and Architectures
- Unsupervised Learning and Clustering for Biomedical Data
- Feature Extraction and Preprocessing of Biosignals
- Hybrid Systems: Combining Neural Nets, Fuzzy Logic, and Expert Systems
- Case Studies: ECG and Cardiac Diagnostic Applications
- Case Studies: EEG, Neurological and Sleep-Related Analyses
- Medical Imaging and Pattern Recognition Applications
- Validation, Performance Evaluation, and Clinical Considerations
- Implementation Issues and Software Tools
- Future Directions and Ethical/Regulatory Considerations
Languages, Platforms & Tools
How It Compares
More application-focused and biomedical-domain specific than Bishop's Pattern Recognition texts (which are more theoretical); complements biomedical signal-processing texts (e.g., Rangayyan) by emphasizing AI/decision-support approaches rather than low-level DSP.
