Bayesian voting of multiple annotators for improved QT interval estimation

Human bias and significant intra- and inter- observer variance exist in electrocardiogram QT interval evaluation. A Bayesian approach (BA) with an informative prior, that combines measures from multiple humans or algorithms as well as contextual information (such as heart rate and signal quality) was developed for inferring the true QT length. The developed method is compared to the mean and median voting approaches by computing the root-mean-square (RMS) error between the computed QT lengths and the reference annotations provided by the 2006 PhysioNet/Computing in Cardiology Challenge. The BA with features can reduces the human RMS error of QT estimates to 6.04ms and 13.97ms for automated algorithms, out-performing the results in the Challenge of 6.67ms and 16.34ms respectively. For three annotators, the BA had a 10.7% improvement over the next best voting strategy for manual annotations, and 14.4% for automated algorithms. For large numbers of annotators, the BA estimates became approximately equal to the best-performing annotator.