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Books > Medicine > General issues > Medical equipment & techniques
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Signal Processing and Machine Learning for Biomedical Big Data (Hardcover)
Loot Price: R6,231
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Signal Processing and Machine Learning for Biomedical Big Data (Hardcover)
Expected to ship within 12 - 17 working days
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Within the healthcare domain, big data is defined as any ``high
volume, high diversity biological, clinical, environmental, and
lifestyle information collected from single individuals to large
cohorts, in relation to their health and wellness status, at one or
several time points.'' Such data is crucial because within it lies
vast amounts of invaluable information that could potentially
change a patient's life, opening doors to alternate therapies,
drugs, and diagnostic tools. Signal Processing and Machine Learning
for Biomedical Big Data thus discusses modalities; the numerous
ways in which this data is captured via sensors; and various sample
rates and dimensionalities. Capturing, analyzing, storing, and
visualizing such massive data has required new shifts in signal
processing paradigms and new ways of combining signal processing
with machine learning tools. This book covers several of these
aspects in two ways: firstly, through theoretical signal processing
chapters where tools aimed at big data (be it biomedical or
otherwise) are described; and, secondly, through application-driven
chapters focusing on existing applications of signal processing and
machine learning for big biomedical data. This text aimed at the
curious researcher working in the field, as well as undergraduate
and graduate students eager to learn how signal processing can help
with big data analysis. It is the hope of Drs. Sejdic and Falk that
this book will bring together signal processing and machine
learning researchers to unlock existing bottlenecks within the
healthcare field, thereby improving patient quality-of-life.
Provides an overview of recent state-of-the-art signal processing
and machine learning algorithms for biomedical big data, including
applications in the neuroimaging, cardiac, retinal, genomic, sleep,
patient outcome prediction, critical care, and rehabilitation
domains. Provides contributed chapters from world leaders in the
fields of big data and signal processing, covering topics such as
data quality, data compression, statistical and graph signal
processing techniques, and deep learning and their applications
within the biomedical sphere. This book's material covers how
expert domain knowledge can be used to advance signal processing
and machine learning for biomedical big data applications.
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