Automatic detection of non-biological artifacts in ECGs acquired during cardiac computed tomography
Bekmukhametov R, Poelsterl S, Allmendinger T, Doan MD, Navab N (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9853 LNCS
Pages Range: 193-208
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Riva del Garda, ITA
ISBN: 9783319461304
DOI: 10.1007/978-3-319-46131-1_24
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Bekmukhametov, R., Poelsterl, S., Allmendinger, T., Doan, M.-D., & Navab, N. (2016). Automatic detection of non-biological artifacts in ECGs acquired during cardiac computed tomography. In Björn Bringmann, Elisa Fromont, Nikolaj Tatti, Volker Tresp, Pauli Miettinen, Bettina Berendt, Gemma Garriga (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 193-208). Riva del Garda, ITA: Springer Verlag.
MLA:
Bekmukhametov, Rustem, et al. "Automatic detection of non-biological artifacts in ECGs acquired during cardiac computed tomography." Proceedings of the 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016, Riva del Garda, ITA Ed. Björn Bringmann, Elisa Fromont, Nikolaj Tatti, Volker Tresp, Pauli Miettinen, Bettina Berendt, Gemma Garriga, Springer Verlag, 2016. 193-208.
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