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Computational Intelligence in Biomedical Engineering

(15 Credits)


To further enhance the students’ skills in biomedical signal and data processing with the principles of computational intelligence as applied to biomedical engineering including cardiology, neurology, biomechanics and movement sciences.

The module will provide the student with a firm grounding in methods and tools for extracting information from biomedical signals and data, it will also introduce the practical implementation of computational intelligence techniques applied to digitally acquired biomedical signals

Learning Outcomes:

At completion, students will be able to:
• Be able to demonstrate a systematic knowledge of the complex physical and physiological principles that underpin the measurement of biomedical signals/ data.
• Be able to demonstrate an advanced understanding of the principles of computational intelligence.
• Be able to systematically apply computational intelligence techniques to extract relevant information from biomedical signal measurements/ data.
• Be able to critically assess the appropriateness of different computational intelligence techniques for various problems in the field.
• Be able to evaluate the effectiveness of techniques applied to biomedical signals/ data against specific benchmarks.
• Be able to participate in a multidisciplinary working group for the systematic design and development of an innovative solution to a practical problem.

Illustrative Bibliography:

- R. Begg, D.T.H. Lai and M. Palaniswami, Computational Intelligence in Biomedical Engineering, CRC Press, 2008,ISBN-13: 978-0-8493-4080-2
- A V Oppenheim & R W Schafer, Discrete-time Digital Signal Processing, 2009, ISBN-13: 978-0131988422 ISBN-10: 0131988425 Edition: 3rd, Prentice-Hall: Englewood Cliffs, NJ
- M. Hagan, H.B. Demuth and M. Beale, Neural Network Design, PWS Publishing Company, ISBN-13: 0-534-94332-2

Selected articles from scientific journals, including:
a. IEEE Transactions of Biomedical Engineering, ISSN: 0018-9294
b. Medical Biological Engineering and Computing, ISSN: 1741-0444 (electronic version)