Published

  1. Aston, P., Lyle J. V., Bonet-Luz E., Huang C. L. H., Zhang Y. Jeevaratnam K., Nandi, M. (2019), Deep learning applied to attractor images derived from ECG signals for detection of genetic mutation, Computing in Cardiology, https://doi.org/10.22489/CinC.2019.097

  2. Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F.I., Samek, W., and Schaeffter, T. (2020), PTB-XL: A Large Publicly Available ECG Dataset. Scientific Data, Volume 7 (1), 154.https://doi.org/10.1038/s41597-020-0495-6.
     
  3. Nagel, C., Pilia, N., Loewe, A., and Dössel, O. (2020), Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort. Current Directions in Biomedical Engineering, Volume 6(3): 20203127, https://doi.org/10.1515/cdbme-2020-3127
     
  4. Strodthoff, N., Wagner, P., Schaeffter, T., and Samek, W. (2020). Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE Journal of Biomedical and.Health Informatics, Volume 25 (5) 1519. https://doi.org/10.1109/JBHI.2020.3022989
     
  5. Pilia, N., Severi, S., Raimann, J. G., Genovesi, S., Dössel, O., Kotanko, P., and Loewe, A. (2020). Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us? APL Bioengineering 4(4), 041501. https://doi.org/10.1063/5.0018504
     
  6. Venton J., Harris P. M., Sundar A., Smith N. A. S., Aston P. J. (2021). Robustness of convolutional neural networks to physiological electrocardiogram noise. Philosophical Transactions of the Royal Society A, Volume 379 Article ID:20200262. https://doi.org/10.1098/rsta.2020.0262.
     
  7. Pilia, N., Nagel, C., Lenis, G., Becker, S., Dössel, O., Loewe, A. (2021). ECGdeli - An open source ECG delineation toolbox for MATLAB. SoftwareX. Volume 13, 100639; https://doi.org/10.1016/j.softx.2020.100639
     
  8. Nagel, C., Schuler, S., Dössel, O., and Loewe, A. (2021). A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations, Medical Image Analysis.Volume 74, 102210. https://doi.org/10.1016/j.media.2021.102210
     
  9. Nagel C, Luongo G, Azzolin L, Schuler S, Dössel O, Loewe A. (2021). Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability. Journal of Clinical Medicine, 10(8):1797.https://doi.org/10.3390/jcm10081797
     
  10. Nagel, C., Dössel, O. & Loewe, A. (2021). Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG. Current Directions in Biomedical Engineering, 7(2), 307-310. https://doi.org/10.1515/cdbme-2021-2078
     
  11. Welle, H., Nagel, C., Loewe, A., Mikut, R., Dössel, O. (2021). Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs. Current Directions in Biomedical Engineering, 7(2), 582-585.https://doi.org/10.1515/cdbme-2021-2148
     
  12. Dössel O., Luongo G., Nagel C., Loewe. A. (2021). Computer Modeling of the Heart for ECG Interpretation—A Review. Hearts.; 2(3):350-368.https://doi.org/10.3390/hearts2030028
     
  13. Venton J.and Aston P. J. (2021) Investigating the Robustness of Deep Learning to Electrocardiogram Noise,Computing in Cardiology, https://openresearch.surrey.ac.uk/esploro/outputs/conferencePaper/Investigating-the-Robustness-of-Deep-Learning/99634465502346?institution=44SUR_INST
     
  14. Gillette, K., Gsell, M. A. F., Prassl, A. J., Karabelas, E., Reiter, U., Reiter, G., Grandits, T., Payer, C., Štern, D., Urschler, M., Bayer, J. D., Augustin, C. M., Neic, A., Pock, T., Vigmond, E. J., & Plank, G. (2021). A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Medical Image Analysis, Volume71, 102080, https://doi.org/10.1016/j.media.2021.102080
     
  15. Gillette, K., Gsell, M. A. F., Strocchi, M., Grandits, T., Neic, A., Manninger, M., Scherr, D., Roney, C. H., Prassl, A. J., Augustin, C. M., Vigmond, E. J., & Plank, G. (2022). A personalized real-time virtual model of whole heart electrophysiology. Frontiers in Physiology, Volume 13, 907190.https://doi.org/10.3389/fphys.2022.907190
     
  16. Gillette, K., Gsell, M. A. F., Bouyssier, J., Prassl, A. J., Neic, A., Vigmond, E. J., Plank, G. (2021). Automated Framework for the Inclusion of a His-Purkinje System in Cardiac Digital Twins of Ventricular Electrophysiology. Annals of Biomedical Engineering, Volume 49(12), 3143–3153, https://doi.org/10.1007/s10439-021-02825-9
     
  17. Bender, J., Nagel, C., Fröhlich, J., Wieners, C., Dössel, O. Loewe, A. (2022). A Large-scale Virtual Patient Cohort to Study ECG Features of Interatrial Conduction Block. Current Directions in Biomedical Engineering, Volume8(2), 97-100. https://doi.org/10.1515/cdbme-2022-1026
     
  18. Karli Gillette, Matthias AF Gsell, Stefan Kurath-Koller, Anton J. Prassl, Gernot Plank (2022) Exploring Role of Accessory Pathway Location in Wolff-Parkinson-White Syndrome in a Model of Whole Heart Electrophysiology Computing in Cardiologyhttps://doi.org/10.22489/CinC.2022.057
     
  19. Nagel, C., Osypka, J., Unger, L., Nairn, D., Luik, A., Wakili, R., Dössel, O. Loewe, A. (2022). Improving Clinical ECG-based Atrial Fibrosis Quantification With Neural Networks Through in silico P waves From an Extensive Virtual Patient Cohort. Computing in Cardiology (CinC).https://doi.org/10.22489/CinC.2022.124
     
  20. Winkler B, Nagel C, Farchmin N, Heidenreich S, Loewe A, Dössel O, Bär M. (2023). Global Sensitivity Analysis and Uncertainty Quantification for Simulated Atrial Electrocardiograms. Metrology. Volume 3(1), 1-28. https://doi.org/10.3390/metrology3010001
     
  21. Luongo, G., Vacanti, G., Nitzke, V., Nairn, D., Nagel, C., Kabiri, D., Almeida, T. P., Soriano, D. C., Rivolta, M. W., Ng, G. A., Dössel, O., Luik, A., Sassi, R., Schmitt, C., Loewe, A. (2022). Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram. Europace, Volume 24(7), 1186–1194. https://doi.org/10.1093/europace/euab322
     
  22. Nagel, C., Espinosa, B. C.,Gillette, K., Gsell, M., Sánchez Arciniegas, J., Plank, G., Dössel, O., Loewe, A. (2023). Comparison of Propagation Models and Forward Calculation Methods on Cellular, Tissue and Organ Scale Atrial Electrophysiology. IEEE Transactions on Biomedical Engineering, Volume 70 (2), 511-522. https://dx.doi.org/10.1109/TBME.2022.3196144
     
  23. Azzolin, L., Nagel, C., Nairn, D,, Sánchez Arciniegas, J,, Zheng, T., Eichenlaub, M.,adidi, A., Dössel, O., Loewe, A. (2021). Automated Framework for the Augmentation of Missing Anatomical Structures and Generation of Personalized Atrial Models from Clinical Data. Computing in Cardiology.http://www.cinc.org/archives/2021/pdf/CinC2021-242.pdf
     
  24. Aston, P. J., Mehari, T., Bosnjakovic, A., Harris, P. M., Sundar, A., Williams, S. E., Dössel, O., Loewe, A., Nagel, C., & Strodthoff, N. (2022). Multi-Class ECG Feature Importance Rankings: Cardiologists vs Algorithms. Computing in Cardiology.https://doi.org/10.22489/CinC.2022.087
     
  25. Venton, J., Gillette, K., Gsell, M., Loewe, A., Nagel, C., Winkler, B., & Wright, L. (2022) Sensitivity Analysis of Electrocardiogram Features to Computational Model Input Parameters. Computing in Cardiology.https://doi.org/10.22489/CinC.2022.024
     
  26. Mehari, T., and Strodthoff, N.. (2022). Self-supervised representation learning from 12-lead ECG data. Computers in Biology and Medicine. Volume141.https://doi.org/10.1016/j.compbiomed.2021.105114
     
  27. Strodthoff, N., Mehari, T., Nagel, C., Aston, P. J., Sundar, A., Graff C., Kanters, J. K., Haverkamp, W., Dössel, O., Loewe, A., Bär, M., Schäffter, T. (2023). PTB-XL+, a comprehensive electrocardiographic feature dataset. Scientific Data. Volume 10, Article Number 279.https://doi.org/10.1038/s41597-020-0495-6
     
  28. Mehari, T, and Strodthoff, N. (2022). Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models. ML4H (Machine Learning for Health), https://arxiv.org/abs/2211.07579
     
  29. Mehari, T, Sundar, A, Bosnjakovic, A, Harris, P, Williams, SE, Loewe, A, Dössel, O, Nagel, C, Strodthoff, N, and Aston, PJ (2023). ECG Feature Importance Rankings: Cardiologists vs. Algorithms.  https://doi.org/10.48550/arXiv.2304.02577
     
  30. K. Gillette, M. A. Gsell, A. J. Prassl, G. Plank (2021). Influence of Electrode Placement on the Morphology of In Silico 12 Lead Electrocardiograms. Computing in Cardiology. https://doi.org/10.5281/zenodo.7941047
     
  31. Karli Gillette, Matthias A.F. Gsell, Claudia Nagel, Jule Bender, Bejamin Winkler, Steven E. Williams, Markus Bär, Tobias Schäffter, Olaf Dössel, Gernot Plank, Axel Loewe (2023). MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations https://doi.org/10.48550/arXiv.2211.15997