The PhysioNet/Computing in Cardiology Challenge 2010: Mind the Gap

摘要

Participants in the 11th annual PhysioNet/CinC Challenge were asked to reconstruct, using any combination of available prior and concurrent information, 30-second segments of ECG, continuous blood pressure waveforms, respiration, and other signals that had been removed from recordings of patients in intensive care units.Fifteen of the 53 participants provided reconstructions for the entire test set of 100 ten-minute recordings. The mean correlation between the segments that had been removed (the “target signals”) and the reconstructions produced using the two most successful methods is 0.9, and the sum of the squared residual errors in these reconstructions is less than 20% of the energy of the target signals.Sources for the most successful methods developed for this challenge have been made available by their authors to support research on robust estimation of parameters derived from unreliable signals, detection of changes in patient state, and recognition of signal corruption.