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Reducing diverse sources of noise in ventricular electrical signals using variational autoencoders
Ruipérez-Campillo, S., Ryser, A., Sutter, T. M., Deb, B., Feng, R., Ganesan, P., Brennan, K. A., Rogers, A. J., Kolk, M. Z. H., Tjong, F. V. Y., Narayan, S. M. & Vogt, J. E., 5 Mar 2026, In: Expert Systems With Applications. 300, 130185.Research output: Contribution to journal › Article › Academic › peer-review
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Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias
Kolk, M. Z. H., Frodi, D. M., Langford, J., Andersen, T. O., Jacobsen, P. K., Risum, N., Tan, H. L., Svendsen, J. H., Knops, R. E., Diederichsen, S. R. Z. G. & Tjong, F. V. Y., 1 Dec 2024, In: npj Digital Medicine. 7, 1, 250.Research output: Contribution to journal › Article › Academic › peer-review
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Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias
DEEP RISK investigators, Raijmakers, F. D., Van Der Lingen, A.-L. C. J., Götte, M. J. W., Selder, J. L., Alvarez-Florez, L., Išgum, I. & Bekkers, E. J., Dec 2024, In: Scientific reports. 14, 1, p. 14889 1 p., 14889.Research output: Contribution to journal › Article › Academic › peer-review
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