Robust detection of heart beats in multimodal data

Ikaro Silva, Benjamin Moody, Joachim Behar, Alistair Johnson, Julien Oster, Gari D Clifford, George B Moody
2015 Physiological Measurement  
This editorial reviews the background issues, the design, the key achievements, and the follow-up research generated as a result of the PhysioNet/Computing in Cardiology (CinC) 2014 Challenge, published in the concurrent special issue of Physiological Measurement. Our major focus was to accelerate the development and facilitate the comparison of robust methods for locating heart beats in long-term multi-channel recordings. A public (training) database consisting of 151,032 annotated beats was
more » ... mpiled from records that contained ECGs as well as pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. A separate hidden test data set (consisting of 152,478 beats) is permanently stored at PhysioNet, and a public framework has been developed to provide researchers the ability to continue to automatically score and compare the performance of their algorithms. A scoring criteria based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity is proposed. The top three scores (as of March 2015) on the hidden test data set were 93.64%, 91.50%, and 90.70%.
doi:10.1088/0967-3334/36/8/1629 pmid:26217894 pmcid:PMC4668947 fatcat:4pkvsehmi5gaznd37bxfg7xcfi