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.
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.