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This book is a practical guide to the field of biomedical signal processing and pattern recognition. The authors provide a self-contained volume that will address all the main issues - e.g., signal peculiarities, popular algorithms, key application examples, etc. - as well as provide an overview of common traps and mistakes and how to avoid them. The book makes extensive use of pseudo-code and code samples. It discusses available open source toolboxes as well. This book will the non-specilist up to speed with regard to relevant signal processing and pattern recognition. More importantly, it will enable the reader to either program the necessary algorithms or to modify existing open source libraries.
This book fills a gap in several new interdisciplinary areas, such as human-machine interaction, affective computing, and computer games, that have as a common task the processing and analysis of biomedical data. Yet, it is also a valuable tool for students at the intermediate and advanced levels in more traditional biomedical pattern recognition.
List of contents
PART I - THE BASICS
1. Introduction and book overview
2. Biomedical signal characteristics
2.1. Time series
2.2. Medical Images
3. Signal Preprocessing
3.1. Frequency domain filters
3.2. Spatial filters
3.3. Referencing
3.4. Image enhancement
3.5. Blind source separation (PCA, ICA, SVD)
4. Domain transformations and typical features
4.1. EEG
4.2. EMG
4.3. ECG
4.4. Medical Images
4.5. Other signals (GSR, nIRS, etc.)
PART II - ALGORITHMS
5. Overview
5.1. Discriminative vs. Generative
5.2. Supervised learning
5.3. Unsupervised learning
5.4. Reinforcement learning
5.5. Transduction
6. Bayesian approaches
7. K-nearest neighbour
8. Linear discriminant analysis
9. Support vector machines
10. Quadratic classifiers
11. Evolutionary Algorithms
12. Artificial neural networks
13. Hidden Markov models
14. Features Selection
14.1. Statistical class separation
14.2. Classifier dependent
15. Adaptive Algorithms
PART III - APPLICATIONS (short literature review + 1 fully worked example including online data and code)
16. EEG
17. EMG
18. ECG
19. Medical Images
APPENDICES (if needed)
mathematical proofs (?)
code samples (?)
About the author
F. Sepulveda:
- Member of the Peer Review College of the UK's Engineering and Physical Sciences Research Council (EPSRC)
- Academic Senate, University of Essex
- Editorial board of the new MDPI journal Computers
- Reviewer board of the Italian Space Agency
- Advisory Committee, Computer Science and Electronic Engineering Conference (CEEC2011)
- Coordinator, Brain-Computer Interfaces Group, University of Essex
- Member of the Intelligent Systems Research Group, University of Essex
- Member, Centre for Computational Intelligence, University of Essex
R. Poli:
- Advisory board, Evolutionary Computation Journal (MIT Press)
- Associate Editor, Journal of Genetic Programming and Evolvable Machines (Springer)
- Editorial board, International Journal of Computational Intelligence Research
- Editorial board, Swarm Intelligence (Springer)
- Editorial board, International Journal of Applied Metaheuristic Computing
- Member of the Peer Review College of the UK's Engineering and Physical Sciences Research Council (EPSRC)
- Member, Brain-Computer Interfaces Group, University of Essex
- Member of the Intelligent Systems Research Group, University of Essex
