Intelligent Biomedical Pattern Recognition: A Practical Guide (Biological and Medical Physics, Biomedical Engineering)
| Author | : | |
| Rating | : | 4.30 (630 Votes) |
| Asin | : | 9400729065 |
| Format Type | : | paperback |
| Number of Pages | : | 280 Pages |
| Publish Date | : | 2016-07-30 |
| Language | : | English |
DESCRIPTION:
The book makes extensive use of pseudo-code and code samples. 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. This book is a practical guide to the field of biomedical signal processing and pattern recognition. It discusses available open source toolboxes as well. 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. This book will the non-specilist up to speed with regard to relevant signal processing and pattern recognition. Yet, it is also a valuable tool for students at the intermediate and advanced levels in more traditional biomedical pattern recognition.. More importantly, it will enable the reader to either program the necessary algorithms or to modify existing open source libraries
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, B
The book makes extensive use of pseudo-code and code samples. – as well as provide an overview of common traps and mistakes and how to avoid them. . 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. It discusses available open source toolboxes as well. The authors provide a self-contained volume that will address all the main issues – e.g., signal peculiarities, popular algorithms, key application examples, etc. This book will the non-specilist up to speed with regard to relevant signal processing and pattern recognition. Yet, it is also a valuable tool for students at the intermediate and advanced levels in more traditional biomedical