Biomedical Measurement Systems and Data Science

Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide.

Michael Insana (Author)

9781107179066, Cambridge University Press

Hardback, published 17 June 2021

400 pages
24.4 x 17 x 2.4 cm, 0.842 kg

'a good introductory text on the technical aspects of analyzing measured data sets for graduate students or advanced undergraduates in a premedical or related curriculum … In an increasingly crowded publication space, this book offers something new and valuable, making implementation of data analysis approaches accessible for biomedical scientists and the engineers who work with them … Highly recommended.' M. R. King, Choice Connect

Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB® scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.

1. Introduction to measurement systems
2. Probability
3. Statistics of random processes
4. Spatiotemporal models of the measurement process
5. Basis decomposition I
6. Basis decomposition II
7. Projection radiography
8. Statistical decision-making
9. Statistical pattern recognition with flow cytometry examples
10. ODE models I, biological systems
11. ODE models II, sensors.

Subject Areas: Biotechnology [TCB], Nanotechnology [TBN], Biophysics [PHVN], Biomedical engineering [MQW], Biomechanics, human kinetics [MFGV]