Liénard-type dynamical system models for the simulation of the human heart atrium

Abstract

on the instantaneous health status of the patient. Most of these data are lost soon after it is recorded. The objective of this project is to develop a computing cluster to record and process all physiological data from all ICU bedside monitors within a hospital. Methods: Custom informatics server software was written to link 10 off-the-shelf commodity computers into a cluster computing system. An SQL database was constructed to organize the physiological and clinical data of patients. A graphical user interface was constructed in MATLAB to allow for easy data inspection, visualization, and markup. Physiological data are distributed to the nodes of this system each night. Results: The informatics cluster computing system has been online since January 2009 at the University of Virginia Medical Center. The system contains 80 processors, 40 GB of RAM and 100 TB of hard-drive capacity, including redundant data storage. Approximately 100 MB of data are recorded per bed per day and are composed of 3 electrocardiograms, chest impedance, oxygen saturation, heart rate, blood pressure, as well as other vital signs and waveforms. The system contains a largely complete physiological record of the approximately 1200 patients admitted to the neonatal ICU, pediatric ICU, and surgical trauma ICU since the system became operational. This system is currently being rolled out at Texas Children's Hospital. Conclusions: We have successfully deployed a cluster computing system to capture and process physiological data from bedside monitors within an ICU environment. We are using this system to develop new metrics for patient health, which can eventually be presented to the physician at the bedside to better guide their care.