Open AccessMedicineEngineeringComputer Science

G. Clifford, Ikaro Silva, Benjamin Moody, Qiao Li, Danesh K. Kella, A. Shahin, T. Kooistra, D. Perry, R. Mark

2015.9.1Computing in Cardiology

DOI: 10.1109/cic.2015.7408639

tlooto Summary

A set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms is provided, and the general research community is challenged to address the issue of false alarm suppression using all available signals.

Abstract

High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered.

Citation format

CLIFFORD, G., et al. The physionet/computing in cardiology challenge 2015: Reducing false arrhythmia alarms in the ICU. Computing in Cardiology, 2015, 2015: 273–276.