PhysioNet, established in 1999 as the NIH-sponsored Research Resource for Complex Physiologic Signals, has attained a preeminent status among data and software resources in biomedicine. Its data archive, PhysioBank, was the first, and remains the world's largest, most comprehensive and most widely used repository of time-varying physiologic signals. Its software collection, PhysioToolkit, supports exploration and quantitative analyses of PhysioBank and similar data with a wide range of well-documented, rigorously tested open-source software that can be run on any platform. PhysioNet's team of researchers leverages results of other funded projects to drive the creation and enrichment of: Data collections that provide increasingly comprehensive, multifaceted views of pathophysiology over long time intervals, such as the MIMIC II (Multiparameter Monitoring in Intensive Care) Database of critical care patients; Analytic methods that lead to more timely and accurate diagnoses of major public health problem (such as life-threatening cardiac arrhythmias, infant apneas and seizures), and elucidation of dynamical changes associated with disease and aging (such as cardiopulmonary interactions during sleep disordered breathing syndromes); User interfaces, reference materials and services that add value and improve accessibility to PhysioNet's data and software (such as PhysioNetWorks, a virtual laboratory for data sharing). Impact: Cited in The White House Fact Sheet on Big Data Across the Federal Government (March 29, 2012), PhysioNet is a proven enabler and accelerator of innovative research by investigators with a diverse range of interests, working on projects made possible by data that are inaccessible otherwise. PhysioNet's world- wide, growing community of over 40,000 researchers, clinicians, educators, students, and medical instrument and software developers, retrieve about 700 GB of data per day. By providing free access to its unique and wide-ranging data and software collections, PhysioNet enables studies that currently result in an impressive average of nearly 130 new publications per month by academic, clinical, and industry-affiliated researchers worldwide.
Specific aims : For the next 5 years we aim to: 1. Sustain and enhance PhysioNet's impact with new technology and data; 2. Drive relevant innovation through a vigorous research program on complex physiologic signals; 3. Stimulate and support a growing community of investigators.

Public Health Relevance

PhysioNet, the Research Resource for Complex Physiological Signals, maintains the world's largest, most comprehensive and most widely used repository of time-varying physiological signals and associated signal- processing software, and makes them freely available to the research community. PhysioNet is a proven enabler and accelerator of innovative biomedical research made possible by data that would be otherwise inaccessible.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM104987-11
Application #
9300973
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2007-09-01
Project End
2019-06-30
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
11
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Beth Israel Deaconess Medical Center
Department
Type
DUNS #
071723621
City
Boston
State
MA
Country
United States
Zip Code
02215
Zuzarte, Ian; Indic, Premananda; Sternad, Dagmar et al. (2018) Quantifying Movement in Preterm Infants Using Photoplethysmography. Ann Biomed Eng :
Pollard, Tom J; Johnson, Alistair E W; Raffa, Jesse D et al. (2018) The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 5:180178
Geovanini, Glaucylara Reis; Wang, Rui; Weng, Jia et al. (2018) Elevations in neutrophils with obstructive sleep apnea: The Multi-Ethnic Study of Atherosclerosis (MESA). Int J Cardiol 257:318-323
Li, Taibo; Matsushima, Minoru; Timpson, Wendy et al. (2018) Epidemiology of patient monitoring alarms in the neonatal intensive care unit. J Perinatol 38:1030-1038
Chang, Joshua; Paydarfar, David (2018) Evolution of extrema features reveals optimal stimuli for biological state transitions. Sci Rep 8:3403
Johnson, Alistair E W; Aboab, Jerome; Raffa, Jesse D et al. (2018) A Comparative Analysis of Sepsis Identification Methods in an Electronic Database. Crit Care Med 46:494-499
Vest, Adriana N; Da Poian, Giulia; Li, Qiao et al. (2018) An open source benchmarked toolbox for cardiovascular waveform and interval analysis. Physiol Meas 39:105004
Costa, Madalena D; Redline, Susan; Davis, Roger B et al. (2018) Heart Rate Fragmentation as a Novel Biomarker of Adverse Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis. Front Physiol 9:1117
Lehman, Li-Wei H; Mark, Roger G; Nemati, Shamim (2018) A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series. IEEE J Biomed Health Inform 22:56-66
Clifford, Gari D; Liu, Chengyu; Moody, Benjamin et al. (2017) AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017. Comput Cardiol (2010) 44:

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