Oversized hardcover, weight: 1840g (please note: extra shipping will be required), xviii + 605 pages, NOT ex-library. Printed and bound in the UK. Interior is clean and bright throughout, with unmarked text, free of inscriptions and stamps, firmly bound. Boards show shelfwear, scuff-marks, rubbed tip of lower outer corner of the front board. Issued without a dust jacket. -- Contents: Preface; Editors; Contributors; I Introduction -- 1. Signal Processing in the Era of Biomedical Big Data / Tiago H. Falk & Ervin Sejdic; 2. Collecting and Making Sense of Big Data for Improved Health Care / Thomas R. Clancy; 3. Big Data Era in Magnetic Resonance Imaging of the Human Brain / Xiaoyu Ding, Elisabeth de Castro Caparelli & Thomas J. Ross; II Signal Processing for Big Data -- 4. Data-Driven Approaches for Detecting and Identifying Anomalous Data Streams / Shaofeng Zou; 5. Time?Frequency Analysis for EEG Quality Measurement and Enhancement with Applications in Newborn EEG Abnormality Detection Multichannel EEG Enhancement and Classification for Newborn Health Outcome Prediction / Boualem Boashash; 6. Active Recursive Bayesian State Estimation for Big Biological Data / Mohammad Moghadamfalahi, Murat Akcakaya & Deniz Erdogmus; 7. Compressive Sensing Methods for Reconstruction of Big Sparse Signals / Ljubisa Stankovic, Milos Dakovic & Isidora Stankovic; 8. Low-Complexity DCT Approximations for Biomedical Signal Processing in Big Data / Renato J. Cintra; 9. Dynamic Processes on Complex Networks / June Zhang & Jos� M.F. Moura; 10. Modeling Functional Networks via Piecewise-Stationary Graphical Models / Hang Yu & Justin Dauwels; 11. Topological Data Analysis of Biomedical Big Data / Angkoon Phinyomark, Esther Iba�ez-Marcelo & Giovanni Petri; 12. Targeted Learning with Application to Health Care Research / Susan Gruber; III Applications of Signal Processing and Machine Learning for Big Biomedical Data -- 13. Scalable Signal Data Processing for Measuring Functional Connectivity in Epilepsy Neurological Disorder / Arthur Gershon; 14. Machine Learning Approaches to Automatic Interpretation of EEGs / Iyad Obeid & Joseph Picone; 15. Information Fusion in Deep Convolutional Neural Networks for Biomedical Image Segmentation / Mohammad Havaei; 16. Automated Biventricular Cardiovascular Modelling from MRI for Big Heart Data Analysis / Kathleen Gilbert; 17. Deep Learning for Retinal Analysis / Henry A. Leopold, John S. Zelek & Vasudevan Lakshminarayanan; 18. Dictionary Learning Applications for HEp-2 Cell Classification / Sadaf Monajemi; 19. Computational Sequence- and NGS-Based MicroRNA Prediction / R.J. Peace & James R. Green; 20. Bayesian Classification of Genomic Big Data / Ulisses M. Braga-Neto; 21. Neuroelectrophysiology of Sleep and Insomnia / Ramiro Chaparro-Vargas; 22. Automated Processing of Big Data in Sleep Medicine / Sara Mariani, Shaun M. Purcell & Susan Redline; 23. Integrating Clinical Physiological Knowledge at the Feature and Classifier Levels in Design of a Clinical Decision Support System for Improved Prediction of Intensive Care Unit Outcome / Ali Jalali; 24. Trauma Outcome Prediction in the Era of Big Data: From Data Collection to Analytics / Shiming Yang, Peter F. Hu & Colin F. Mackenzie; 25. Enhancing Medical Problem Solving through the Integration of Temporal Abstractions with Bayesian Networks in Time-Oriented Clinical Domains / Kalia Orphanou, Athena Stassopoulou & Elpida Keravnou; 26. Big Data in Critical Care Using Artemis / Carolyn McGregor; 27. Improving Neurorehabilitation of the Upper Limb through Big Data / Jos� Zariffa; 28. Multimodal Ambulatory Fall Risk Assessment in the Era of Big Data / Mina Nouredanesh & James Tung; Index. N� de ref. del art�culo 007119

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T�tulo: Signal Processing and Machine Learning for ...

Editorial: CRC Press

A�o de publicaci�n: 2018

Encuadernaci�n: Hardcover

Condici�n: Very Good

Edici�n: 1st Edition

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Sinopsis:

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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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.

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