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A self contained text covering topics from basic biomedical signal characteristics to applications
Offers pseudo-code and worked examples enabling the implementation of customised pattern recognition software
Covers key applications and illustrates real online data and sample code in a popular, user-friendly programming language
Provides up-to date algorithms relevant to the field and materials on adaptive algorithms and open source toolboxes
Includes “Tricks and Pitfalls” sections
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.
Content Level »Graduate
Keywords »Biological signal patterns - How to interpret complex biomedical signals - Human-machine interaction - Image analysis - Machine learning - Text on biomedical pattern analysis - User manual for open source toolboxes
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