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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.
Biomedical Signal Analysis for Connected Healthcare provides
rigorous coverage on several generations of techniques, including
time domain approaches for event detection, spectral analysis for
interpretation of clinical events of interest, time-varying signal
processing for understanding dynamical aspects of complex
biomedical systems, the application of machine learning principles
in enhanced clinical decision-making, the application of sparse
techniques and compressive sensing in providing low-power
applications that are essential for wearable designs, the emerging
paradigms of the Internet of Things, and connected healthcare.
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