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Cover image for Practical biomedical signal analysis using MATLAB
Title:
Practical biomedical signal analysis using MATLAB
Series:
Series in medical physics and biomedical engineering
Publication Information:
Boca Raton, FL. : Taylor & Francis, 2012.
Physical Description:
xx, 294 p. : ill. (some col.) ; 25 cm.
ISBN:
9781439812020
General Note:
"A CRC title."

Available:*

Library
Item Barcode
Call Number
Material Type
Item Category 1
Status
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30000010273579 QP341 B5949 2012 Open Access Book Book
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Summary

Summary

Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data.

The first several chapters of the text describe signal analysis techniques--including the newest and most advanced methods--in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice.

A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.


Author Notes

K.J. Blinowska is a professor at University of Warsaw, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. She has been at the forefront in the development of new advanced time-series methods for research and clinical applications.

J. Żygierewicz is an assistant professor at University of Warsaw. His research focuses on time-frequency analysis of EEG and MEG signals, statistical analysis of event-related synchronization and desynchronization in EEG and MEG, and realistic neuronal network models that provide insight into the mechanisms underlying the effects observed in EEG and MEG signals.


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