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 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

Biomedical Signal Analysis Comprehensive resource covering recent developments, applications of current interest, and advanced techniques for biomedical signal analysis Biomedical Signal Analysis provides extensive insight into digital signal processing techniques for filtering, identification, characterization, classification, and analysis of biomedical signals with the aim of computer-aided diagnosis, taking a unique approach by presenting case studies encountered in the authors’ research work. Each chapter begins with the statement of a biomedical signal problem, followed by a selection of real-life case studies and illustrations with the associated signals. Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple “textbook” methods, followed by more sophisticated research-informed approaches. Each chapter concludes with solutions to practical applications. Illustrations of real-life biomedical signals and their derivatives are included throughout. The third edition expands on essential background material and advanced topics without altering the underlying pedagogical approach and philosophy of the successful first and second editions. The book is enhanced by a large number of study questions and laboratory exercises as well as an online repository with solutions to problems and data files for laboratory work and projects. Biomedical Signal Analysis provides theoretical and practical information on: The origin and characteristics of several biomedical signals Analysis of concurrent, coupled, and correlated processes, with applications in monitoring of sleep apnea Filtering for removal of artifacts, random noise, structured noise, and physiological interference in signals generated by stationary, nonstationary, and cyclostationary processes Detection and characterization of events, covering methods for QRS detection, identification of heart sounds, and detection of the dicrotic notch Analysis of waveshape and waveform complexity Interpretation and analysis of biomedical signals in the frequency domain Mathematical, electrical, mechanical, and physiological modeling of biomedical signals and systems Sophisticated analysis of nonstationary, multicomponent, and multisource signals using wavelets, time-frequency representations, signal decomposition, and dictionary-learning methods Pattern classification and computer-aided diagnosis Biomedical Signal Analysis is an ideal learning resource for senior undergraduate and graduate engineering students. Introductory sections on signals, systems, and transforms make this book accessible to students in disciplines other than electrical engineering.

In the past few years Biomedical Engineering has received a great deal of attention as one of the emerging technologies in the last decade and for years to come, as witnessed by the many books, conferences, and their proceedings. Media attention, due to the applications-oriented advances in Biomedical Engineering, has also increased. Much of the excitement comes from the fact that technology is rapidly changing and new technological adventures become available and feasible every day. For many years the physical sciences contributed to medicine in the form of expertise in radiology and slow but steady contributions to other more diverse fields, such as computers in surgery and diagnosis, neurology, cardiology, vision and visual prosthesis, audition and hearing aids, artificial limbs, biomechanics, and biomaterials. The list goes on. It is therefore hard for a person unfamiliar with a subject to separate the substance from the hype. Many of the applications of Biomedical Engineering are rather complex and difficult to understand even by the not so novice in the field. Much of the hardware and software tools available are either too simplistic to be useful or too complicated to be understood and applied. In addition, the lack of a common language between engineers and computer scientists and their counterparts in the medical profession, sometimes becomes a barrier to progress.