TY - GEN
T1 - Automatic detection of non-biological artifacts in ECGs acquired during cardiac computed tomography
AU - Bekmukhametov, Rustem
AU - Pölsterl, Sebastian
AU - Allmendinger, Thomas
AU - Doan, Minh Duc
AU - Navab, Nassir
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Cardiac computed tomography
KW - Electrocardiography
KW - Prospective ECG gating
UR - http://www.scopus.com/inward/record.url?scp=84988614941&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46131-1_24
DO - 10.1007/978-3-319-46131-1_24
M3 - Conference contribution
AN - SCOPUS:84988614941
SN - 9783319461304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 193
EP - 208
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
A2 - Bringmann, Björn
A2 - Fromont, Elisa
A2 - Tatti, Nikolaj
A2 - Tresp, Volker
A2 - Miettinen, Pauli
A2 - Berendt, Bettina
A2 - Garriga, Gemma
PB - Springer Verlag
T2 - 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
Y2 - 19 September 2016 through 23 September 2016
ER -