Signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments

An algorithm to detect poor quality ECGs collected in low-resource environments is described (and was entered in the PhysioNet/Computing in Cardiology Challenge 2011 `Improving the quality of ECGs collected using mobile phones´). The algorithm is based on previously published signal quality metrics, with some novel additions, designed for intensive care monitoring. The algorithms have been adapted for use on short (10s) 12-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Six metrics are produced for each channel (72 features in all) and presented to machine learning algorithms for training on the provided labeled data (Set-a) for the challenge. (Binary labels were available, indicating whether the data were acceptable or unacceptable for clinical interpretation.) We re-annotated all the data in Set-a as well as those in Set-b (the test data) using two independent annotators, and a third for adjudication of differences. Events were balanced and the 1000 subjects in Set-a were used to train the classifiers. We compared four classifiers: Linear Discriminant Analysis, Naıve Bayes, a Support Vector Machine (SVM) and a Multi-Layer Perceptron artificial neural network classifiers. The SVM and MLP provided the best (and almost equivalent) classification accuracies of 99% on the training data (Set-a) and 95% on the test data (Set-b). The binary classification results (acceptable or unacceptable) were then submitted as an entry into the PhysioNet Computing in Cardiology Competition 2011. Before the competition deadline, we scored 92.6% on the unseen test data (0.6% less than the winning entry). After improving labelling inconsistencies and errors we achieved 94.0%, the highest overall score of all competition entrants.