Signal Processing Of Random Physiological Signals

Foundations of BioSignal Processing presents the most widely used techniques in signal and system analysis. Specifically, the book is concerned with methods of characterizing signals and systems. Author Charles Lessard provides students and researchers an understanding of the time and frequency domain processes which may be used to evaluate random physiological signals such as brainwave, sleep, respiratory sounds, heart valve sounds, electromyograms, and electro-oculograms.Another aim of the book is to have the students evaluate actual mammalian data without spending most or all of their time writing software programs. Lessard recommends the DADiSP digital signal processing software, which allows students to view process steps in a real-time window with little training. Extensive programming ability is not necessary if an individual wishes to apply basic signal processing principles. However, individuals should have sufficient working knowledge of mathematics through calculus, some physiology, and be familiar with the elements of circuit theory (both loop and node equations for passive and active circuits).

This lecture book is intended to be an accessible and comprehensive introduction to random signal processing with an emphasis on the real-world applications of biosignals. Although the material has been written and developed primarily for advanced undergraduate biomedical engineering students it will also be of interest to engineers and interested biomedical professionals of any discipline seeking an introduction to the field. Within education, most biomedical engineering programs are aimed to provide the knowledge required of a graduate student while undergraduate programs are geared toward designing circuits and of evaluating only the cardiac signals. Very few programs teach the processes with which to evaluate brainwave, sleep, respiratory sounds, heart valve sounds, electromyograms, electro-oculograms, or random signals acquired from the body. The primary goal of this lecture book is to help the reader understand the time and frequency domain processes which may be used and to evaluate random physiological signals. A secondary goal is to learn the evaluation of actual mammalian data without spending most the time writing software programs. This publication utilizes “DADiSP”, a digital signal processing software, from the DSP Development Corporation.

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

Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. * Multiple color illustrations are integrated in the text * Includes an introduction to biomedical signals, noise characteristics, and recording techniques * Basics and background for more advanced topics can be found in extensive notes and appendices * A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670

Technology is essential to the delivery of health care but it is still only a tool that needs to be deployed wisely to ensure beneficial outcomes at reasonable costs. Among various categories of health technology, medical equipment has the unique distinction of requiring both high initial investments and costly maintenance during its entire useful life. This characteristic does not, however, imply that medical equipment is more costly than other categories, provided that it is managed properly. The foundation of a sound technology management process is the planning and acquisition of equipment, collectively called technology incorporation. This lecture presents a rational, strategic process for technology incorporation based on experience, some successful and many unsuccessful, accumulated in industrialized and developing countries over the last three decades. The planning step is focused on establishing a Technology Incorporation Plan (TIP) using data collected from an audit of existing technology, evaluating needs, impacts, costs, and benefits, and consolidating the information collected for decision making. The acquisition step implements TIP by selecting equipment based on technical, regulatory, financial, and supplier considerations, and procuring it using one of the multiple forms of purchasing or agreements with suppliers. This incorporation process is generic enough to be used, with suitable adaptations, for a wide variety of health organizations with different sizes and acuity levels, ranging from health clinics to community hospitals to major teaching hospitals and even to entire health systems. Such a broadly applicable process is possible because it is based on a conceptual framework composed of in-depth analysis of the basic principles that govern each stage of technology lifecycle. Using this incorporation process, successful TIPs have been created and implemented, thereby contributing to the improvement of healthcare services and limiting the associated expenses. Table of Contents: Introduction / Conceptual Framework / The Incorporation Process / Discussion / Conclusions

Physiology is a set of processes that maintain homeostasis, and physiological measurement is a means of observing these processes. Systems theory and signal processing offer formal tools for the study of processes and measured quantities. This book shows that systems modeling can be used to develop simulations of physiological systems, which use formal relations between the underlying processes and the observed measurements. The inverse of such relations suggest signal processing tools that can be applied to interpret experimental data. Both signal processing and systems modeling are invaluable in the study of human physiology. Discussing signal processing techniques ranging from filtering and spectrum analysis to wavelet analysis, the book uses graphs and analogies to supplement the mathematics and make the book more accessible to physiologists and more interesting to engineers. Physiological systems modeling helps in both gaining insight and generating methods of analysis. This book shows how numerical computation with graphical display, haptics and multimedia can be used to simulate physiological systems. In this third edition the simulations are more closely related to clinical examination and experimental physiology than in previous editions. Detailed models of nerve and muscle at the cellular and systemic levels, and simplified models of cardiovascular blood flow provide examples for the mathematical methods and computer simulations. Several of the models are sufficiently sophisticated to be of value in understanding real world issues like neuromuscular disease. The book features expanded problem sets and a link to extra downloadable material containing simulation programs that are solutions to the theory developed in the text.

This book constitutes the proceedings of the First International Conference on Physiological Computing Systems, PhyCS 2014, held in Lisbon, Portugal, in January 2014. The 10 papers presented in this volume were carefully reviewed and selected from 52 submissions. They are organized in topical sections named: methodologies and methods; devices; applications; and human factors.