Signal Processing For Neuroscientists A Companion Volume
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Signal Processing for Neuroscientists, A Companion Volume
The popularity of signal processing in neuroscience is increasing, and with the current availability and development of computer hardware and software, it is anticipated that the current growth will continue. Because electrode fabrication has improved and measurement equipment is getting less expensive, electrophysiological measurements with large numbers of channels are now very common. In addition, neuroscience has entered the age of light, and fluorescence measurements are fully integrated into the researcher's toolkit. Because each image in a movie contains multiple pixels, these measurements are multi-channel by nature. Furthermore, the availability of both generic and specialized software packages for data analysis has altered the neuroscientist's attitude toward some of the more complex analysis techniques. This book is a companion to the previously published Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals, which introduced readers to the basic concepts. It discusses several advanced techniques, rediscovers methods to describe nonlinear systems, and examines the analysis of multi-channel recordings. - Covers the more advanced topics of linear and nonlinear systems analysis and multi-channel analysis - Includes practical examples implemented in MATLAB - Provides multiple references to the basics to help the student
Signal Processing for Neuroscientists
Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise section developed by the author. Since the modeling of systems and signal analysis are closely related, integrated presentation of these topics using identical or similar mathematics presents a didactic advantage and a significant resource for neuroscientists with quantitative interest. Although each of the topics introduced could fill several volumes, this book provides a fundamental and uncluttered background for the non-specialist scientist or engineer to not only get applications started, but also evaluate more advanced literature on signal processing and modeling. - Includes an introduction to biomedical signals, noise characteristics, recording techniques, and the more advanced topics of linear, nonlinear and multi-channel systems analysis - Features new chapters on the fundamentals of modeling, application to neuronal modeling, Kalman filter, multi-taper power spectrum estimation, and practice exercises - Contains the basics and background for more advanced topics in extensive notes and appendices - Includes practical examples of algorithm development and implementation in MATLAB - Features a companion website with MATLAB scripts, data files, figures and video lectures
Developments and Applications for ECG Signal Processing
Author: Joao Paulo do Vale Madeiro
language: en
Publisher: Academic Press
Release Date: 2018-11-29
Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition covers reliable techniques for ECG signal processing and their potential to significantly increase the applicability of ECG use in diagnosis. This book details a wide range of challenges in the processes of acquisition, preprocessing, segmentation, mathematical modelling and pattern recognition in ECG signals, presenting practical and robust solutions based on digital signal processing techniques. Users will find this to be a comprehensive resource that contributes to research on the automatic analysis of ECG signals and extends resources relating to rapid and accurate diagnoses, particularly for long-term signals. Chapters cover classical and modern features surrounding f ECG signals, ECG signal acquisition systems, techniques for noise suppression for ECG signal processing, a delineation of the QRS complex, mathematical modelling of T- and P-waves, and the automatic classification of heartbeats. - Gives comprehensive coverage of ECG signal processing - Presents development and parametrization techniques for ECG signal acquisition systems - Analyzes and compares distortions caused by different digital filtering techniques for noise suppression applied over the ECG signal - Describes how to identify if a digitized ECG signal presents irreversible distortion through analysis of its frequency components prior to, and after, filtering - Considers how to enhance QRS complexes and differentiate these from artefacts, noise, and other characteristic waves under different scenarios