Signal Processing and Machine Learning for Biomedical Big Data

Nonfiction, Health & Well Being, Medical, Medical Science, Biotechnology, Science & Nature, Science, Biological Sciences, Technology, Electricity
Cover of the book Signal Processing and Machine Learning for Biomedical Big Data by , CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781351061216
Publisher: CRC Press Publication: July 4, 2018
Imprint: CRC Press Language: English
Author:
ISBN: 9781351061216
Publisher: CRC Press
Publication: July 4, 2018
Imprint: CRC Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from CRC Press

Cover of the book Lubricant Additives by
Cover of the book Feynman Lectures On Gravitation by
Cover of the book Utilisation of Bioactive Compounds from Agricultural and Food Production Waste by
Cover of the book Find Your Way Around JCT 98 by
Cover of the book Medical Big Data and Internet of Medical Things by
Cover of the book Many-Body Methods for Atoms and Molecules by
Cover of the book Routledge Revivals: Easy Lessons in Einstein (1922) by
Cover of the book Human Factors and Ergonomics of Prehospital Emergency Care by
Cover of the book Collaborative Construction Information Management by
Cover of the book Intelligent Systems for Engineers and Scientists by
Cover of the book Handbook of Comparative Pharmacokinetics and Residues of Veterinary Antimicrobials by
Cover of the book Quality and Preservation of Fruits by
Cover of the book Clark's Essential PACS, RIS and Imaging Informatics by
Cover of the book The Sexual Health of Men by
Cover of the book A Tour through Graph Theory by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy