ECG artefact detection using ensemble decision trees

Abstract

This paper describes a novel method for artefact detection in electrocardiogram (ECG) signals. ECG analysis algorithms require a relatively clean dataset. Therefore, data corrupted by artefacts should either be filtered or discarded. The proposed method can be situated in the second class, since it identifies contaminated segments that can later be discarded from further analysis. The dataset used in this study contains 16 single lead ECG recordings, segmented in intervals of 60 seconds. Each segment is labeled either clean or contaminated by a medical doctor. Only 3.2% of the data is contaminated. The segments are characterized by features derived from their autocorrelation function (ACF). Due to its effectiveness in skewed datasets, the RUSBoost algorithm is then used for classification. Results show an accuracy of 99.85%, a sensitivity of 100% and a specificity of 95.51%. This suggests that the proposed method could be of great help for future ECG processing.