@article{
 title = {The PhysioNet/Computing in Cardiology Challenge 2015: Reducing False Arrhythmia Alarms in the ICU.},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {273-276},
 volume = {2015},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/27331073,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4910643},
 month = {9},
 publisher = {NIH Public Access},
 id = {4743c7a8-2e17-365d-9f7b-8a180293c500},
 created = {2017-03-01T11:48:22.000Z},
 accessed = {2017-03-01},
 file_attached = {true},
 profile_id = {304786e8-5116-360a-80be-e62833097578},
 group_id = {d7b44578-07c1-3210-ae74-3bcd7f980767},
 last_modified = {2017-03-14T15:45:25.917Z},
 read = {false},
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 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. A collection of 750 data segments was made available for training and a set of 500 was 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.},
 bibtype = {article},
 author = {Clifford, Gari D and Silva, Ikaro and Moody, Benjamin and Li, Qiao and Kella, Danesh and Shahin, Abdullah and Kooistra, Tristan and Perry, Diane and Mark, Roger G},
 journal = {Computing in cardiology}
}

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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. A collection of 750 data segments was made available for training and a set of 500 was 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. 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