Robust detection of heart beats in multimodal data: The PhysioNet/Computing in Cardiology Challenge 2014

The 15th annual PhysioNet/CinC Challenge aims to encourage the exploration of robust methods for locating heart beats in continuous long-term data from bedside monitors and similar devices that record not only ECG but usually other physiologic signals as well, including pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. Our goal is to accelerate development of open-source research tools that can reliably, efficiently, and automatically analyze data such as that contained in the MIMIC II Waveform Database, making use of all relevant information. Data for this Challenge are 10-minute (or occasionally shorter) excerpts (“records”) of longer multi-parameter recordings of human adults, including patients with a wide range of problems as well as healthy volunteers. Each record contains four to eight signals; the first is an ECG signal in each case, but the others are a variety of simultaneously recorded physiologic signals that may be useful for robust beat detection. We prepared and posted 100 training records, and retained 300 hidden test records for evaluation of Challenge entries. A total of 1,332 entries from 60 teams were processed during the challenge period.