Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis

Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emer-gency event prediction. Specifically, improvements in ma-chine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emer-gencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environment, this work focuses on developing a data repository for bio-signals collected from a hospital's intensive care init (ICU). Specifically, we design our data collection system to effectively store data from at-bed patient monitors and also integrate sensing information from bed-embedded sensing platforms, which allow filtering of noisy bio-signal samples caused by motion artifacts.