Using machine learning to detect problems in ECG data collection

Abstract

We describe a data-driven approach, using a combination of machine learning algorithms to solve the 2011 Physionet/Computing in Cardiology (CinC) challenge — identifying data collection problems at 12 leads electrocardiography (ECG). Our data-driven approach reaches an internal (cross-validation) accuracy of almost 93% on the training set, and accuracy of 91.2% on the test set.