Developing Early Cardiac Arrest Detection in the Pediatric Cardiac Intensive Care Unit using BedMaster Streaming Data

Po-Yen Wu1, Li Tong1, Hang Wu, Janani Venugopalan1, May D. Wang2

  • 1Georgia Institute of Technology
  • 2Georgia Tech and Emory University

Details

08:15 - 08:30 | Wed 17 Aug | Grand Republic D | WeAT19.2

Session: Research and Innovation Forum. Needs and Trends in Algorithms for P-Health Solutions Addressing CVD Management

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Abstract

The goal of this project is to analyze BedMaster streaming data for early cardiac arrest detection in the pediatric cardiac intensive care unit (CICU). Cardiac arrest is a common complication with low survival-to-hospital-discharge rates due to neurodevelopmental issues and other secondary organ damage post the episode. The main challenge is the abruptness of a cardiac arrest episode. In this project, we will develop a computational pipeline starting from high-resolution streaming data acquisition using the BedMaster system, followed by extracting cardiac arrest-related features, and constructing predictive models for early cardiac arrest detection.