BeatDB : an end-to-end approach to unveil saliencies from massive signal data sets

Abstract

Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months. In response we design a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining knowledge from waveforms. BeatDB radically shrinks the time an investigation takes by: * supporting fast, flexible investigations by offering a multi-level parameterization, allowing the user to define the condition to predict, the features, and many other investigation parameters. * precomputing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates. In this thesis, we present BeatDB and demonstrate how it supports flexible investigations on the entire set of arterial blood pressure data in the MIMIC II Waveform Database, which contains over 5000 patients and 1 billion of blood pressure beats. We focus on the usefulness of wavelets as features in the context of blood pressure prediction and use Gaussian process to accelerate the search of the feature yielding the highest AUROC.