You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018

Mohammad Ghassemi1, Benjamin Moody2, Li-wei Lehman1, Christopher Song3, Qiao Li4, Haoqi Sun5, Brandon Westover5, Gari Clifford6
1Massachusetts Institute of Technology, 2MIT, 3Johns Hopkins University, 4Emory University, 5Harvard University, 6Emory University and Georgia Institute of Technology


Abstract

One of the more well-studied sleep disorders is Obstructive Sleep Apnea Hypopnea Syndrome (or simply, apnea). Apneas are characterized by a complete collapse of the airway, leading to awakening, and consequent disturbances of sleep. While apneas are arguably the best understood of sleep disturbances, they are not the only cause of disturbance. Sleep arousals can also be spontaneous, result from teeth grinding, partial airway obstructions, or even snoring. In this year's PhysioNet Challenge we will use a variety of physiological signals, collected during polysomnographic sleep studies, to detect these other sources of arousal (non-apnea) during sleep. Data for this challenge were contributed by the Massachusetts General Hospital’s (MGH) Computational Clinical Neurophysiology Laboratory (CCNL), and the Clinical Data Animation Laboratory (CDAC). The dataset includes 1,985 subjects which were monitored at an MGH sleep laboratory for the diagnosis of sleep disorders. The data were partitioned into balanced training (n = 994), and test sets (n = 989). The goal of the challenge is use information from the available signals to correctly classify target arousal regions. For the purpose of the Challenge, target arousals are defined as regions where either of the following conditions were met: i) From 2 seconds before a RERA arousal begins, up to 10 seconds after it ends or, ii) From 2 seconds before a non-RERA, non-apnea arousal begins, up to 2 seconds after it ends. The winner will be the team that manages to score the highest area under the precision-recall curve on the test data.