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Signal Processing and Machine Learning for Biomedical Big Data

Editat de Ervin Sejdic, Tiago H. Falk
en Limba Engleză Hardback – 5 iul 2018
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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Specificații

ISBN-13: 9781498773454
ISBN-10: 1498773451
Pagini: 624
Ilustrații: 216
Dimensiuni: 210 x 280 x 37 mm
Greutate: 1.72 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Cuprins

An Introduction to big data in medicine. Big heart data. Predicting asthma-related emergency department visits using big data. Fall detection in homes of older adults using Microsoft Kinect. Visualization analysis for big data in computational cyberpsychology. Heart beats in the cloud. Big Data approaches to trauma outcome prediction. The TUH EEG CORPUS. Big Data reduction using RBFNN. Systems Biology and brain activity. Signal processing to make sense of noisy medical Big Data. Prarallel randomly compressed cubes. Big Data analysis with signal on graphs. Outlying sequence detection in large data sets. Breaking the curse of dimensionality using decompositions. Sparse Fourier transform. Modeling and optimization learning tools for big data analytics. Parallel processing for real-time biomedical big data. Heart beats in the cloud.

Notă biografică

Ervin Sejdic is currently an Assistant Professor with the Department of Electrical Engineering and Biomedical Engineering at the University of Pittsburg. He has extensive research experience in biomedical and theoretical signal processing, swallowing difficulties, gait and balance. assistive technologies, rehabilitation engineering, anticipatory medical devices, and advanced information systems in medicine.
Tiago Falk is the founder and director of the Multimodal Signal Analysis and Enhancement Lab at the University of Quebec in Montreal. His work on signal processing for big multimedia and biomedical data has engenered numerous awards, including the 2015 CMBES Early Career Award and the 2014 WearHacks Creativity Award and the IEEE Kingston Section Ph.D Excellence Award.

Descriere

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals.

It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.