False alarm reduction in critical care

• Type of Material: Article
• Authors: Gari Clifford, Emory University; Ikaro Silva, Massachusetts Institute of Technology; Benjamin Moody, Massachusetts Institute of Technology; Qiao Li, Emory University; Danesh Kella, Emory University; Abdullah Chahin, Harvard University; Tristan Kooistra, Harvard University; Diane Perry, Harvard University; Rogers G. Mark, Massachusetts Institute of Technology
• Language: English
• Date: 2016-07-25
• Publisher: IOP Publishing
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Institute of Physics and Engineering in Medicine.
• Final Published Version (URL): http://dx.doi.org/10.1088/0967-3334/37/8/E5
• Title of Journal or Parent Work: Physiological Measurement
• ISSN: 0967-3334
• Volume: 37
• Issue: 8
• Start Page: E5
• End Page: E23
• Grant/Funding Information: This work was funded in part by the National Institutes of Health, grant R01-GM104987 and by the National Institute of General Medical Sciences, under NIH cooperative agreement U01-EB-008577 and NIH grant R01-EB-001659.
• Keywords: Science & Technology; ARTERIAL-BLOOD PRESSURE; UNIT; ARRHYTHMIA ALARMS; TIME; Technology; ICU; Biophysics; Engineering; SIGNAL QUALITY INDEXES; Engineering, Biomedical; Physiology; QRS DETECTION ALGORITHM; FETAL ECG EXTRACTION; MACHINE LEARNING APPROACH; critical care; arrhythmia; CLASSIFICATION; false alarm reduction; Life Sciences & Biomedicine
• Research Categories: Biology, Bioinformatics; Engineering, Biomedical

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 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 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. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.