|R. Bekmukhametov, S. Pölsterl, T. Allmendinger, M. Doan, N. Navab
Automatic Detection of Non-Biological Artifacts in ECGs Acquired During Cardiac Computed Tomography
Machine Learning and Knowledge Discovery in Databases (bib)
Cardiac computed tomography is a non-invasive technique to image the beating heart. One of the main concerns during the procedure is the total radiation dose imposed on the patient. Prospective electrocardiographic (ECG) gating methods may notably reduce the radiation exposure. However, very few investigations address accompanying problems encountered in practice. Several types of unique non-biological factors, such as the dynamic electrical field induced by rotating components in the scanner, influence the ECG and can result in artifacts that can ultimately cause prospective ECG gating algorithms to fail. In this paper, we present an approach to automatically detect non-biological artifacts within ECG signals, acquired in this context. Our solution adapts discord discovery, robust PCA, and signal processing methods for detecting such disturbances. It achieved an average area under the precision-recall curve (AUPRC) and receiver operating characteristics curve (AUROC) of 0.996 and 0.997 in our cross-validation experiments based on 2,581 ECGs. External validation on a separate hold-out dataset of 150 ECGs, annotated by two domain experts (88% inter-expert agreement), yielded average AUPRC and AUROC scores of 0.890 and 0.920. Our solution is deployed to automatically detect non-biological anomalies within a continuously updated database, currently holding over 120,000 ECGs.
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