D. U. J. Keller

709 total citations
22 papers, 408 citations indexed

About

D. U. J. Keller is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, D. U. J. Keller has authored 22 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cardiology and Cardiovascular Medicine, 6 papers in Molecular Biology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in D. U. J. Keller's work include Cardiac electrophysiology and arrhythmias (16 papers), ECG Monitoring and Analysis (8 papers) and Cardiovascular Function and Risk Factors (8 papers). D. U. J. Keller is often cited by papers focused on Cardiac electrophysiology and arrhythmias (16 papers), ECG Monitoring and Analysis (8 papers) and Cardiovascular Function and Risk Factors (8 papers). D. U. J. Keller collaborates with scholars based in Germany, United States and United Kingdom. D. U. J. Keller's co-authors include Olaf Dössel, Gunnar Seemann, F. Weber, Reza Razavi, Kawal Rhode, Martin Krueger, Daniel L. Weiß, Aruna Arujuna, Mark O’Neill and Matthias Reumann and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Medical Imaging and European Urology.

In The Last Decade

D. U. J. Keller

22 papers receiving 403 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
D. U. J. Keller Germany 11 352 64 53 38 33 22 408
Fernando O. Campos Austria 15 518 1.5× 74 1.2× 63 1.2× 62 1.6× 43 1.3× 42 592
Dongdong Deng China 10 567 1.6× 58 0.9× 114 2.2× 68 1.8× 27 0.8× 35 691
Caroline Mendonça Costa United Kingdom 11 347 1.0× 41 0.6× 54 1.0× 45 1.2× 20 0.6× 29 404
William H. Franceschi United States 7 335 1.0× 43 0.7× 34 0.6× 26 0.7× 13 0.4× 8 394
F.J. Claydon United States 7 276 0.8× 30 0.5× 36 0.7× 38 1.0× 31 0.9× 33 322
S Baruffi Cyprus 7 367 1.0× 68 1.1× 83 1.6× 51 1.3× 49 1.5× 9 469
Peter Malamas United States 5 310 0.9× 46 0.7× 93 1.8× 51 1.3× 16 0.5× 6 380
S Spaggiari Hungary 4 259 0.7× 29 0.5× 71 1.3× 38 1.0× 34 1.0× 7 311
Steffen Schuler Germany 12 368 1.0× 35 0.5× 58 1.1× 83 2.2× 16 0.5× 40 465
Ali Gharaviri Netherlands 13 431 1.2× 30 0.5× 36 0.7× 34 0.9× 10 0.3× 32 532

Countries citing papers authored by D. U. J. Keller

Since Specialization
Citations

This map shows the geographic impact of D. U. J. Keller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by D. U. J. Keller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. U. J. Keller more than expected).

Fields of papers citing papers by D. U. J. Keller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. U. J. Keller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by D. U. J. Keller. The network helps show where D. U. J. Keller may publish in the future.

Co-authorship network of co-authors of D. U. J. Keller

This figure shows the co-authorship network connecting the top 25 collaborators of D. U. J. Keller. A scholar is included among the top collaborators of D. U. J. Keller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with D. U. J. Keller. D. U. J. Keller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Meurette, G., Laurent Siproudhis, Anne‐Marie Leroi, et al.. (2021). Sacral neuromodulation with the InterStim™ system for faecal incontinence: results from a prospective French multicentre observational study. Colorectal Disease. 23(6). 1463–1473. 8 indexed citations
2.
3.
Krueger, Martin, D. U. J. Keller, Fredrik Holmqvist, et al.. (2013). In-silico modeling of atrial repolarization in normal and atrial fibrillation remodeled state. Medical & Biological Engineering & Computing. 51(10). 1105–1119. 39 indexed citations
4.
Seemann, Gunnar, et al.. (2013). Variation of Human Ventricular Iks Heterogeneities to Reconstruct Measured Multi-Channel ECG Data. Biomedizinische Technik/Biomedical Engineering. 58 Suppl 1. 1 indexed citations
5.
Seemann, Gunnar, et al.. (2013). Evaluating body surface ECG differences of simulated long-QT syndromes. 345–348. 1 indexed citations
6.
Krueger, Martin, Gunnar Seemann, Kawal Rhode, et al.. (2012). Personalization of Atrial Anatomy and Electrophysiology as a Basis for Clinical Modeling of Radio-Frequency Ablation of Atrial Fibrillation. IEEE Transactions on Medical Imaging. 32(1). 73–84. 65 indexed citations
7.
Keller, D. U. J., et al.. (2011). Influence of <formula formulatype="inline"><tex Notation="TeX">${I_{Ks}}$</tex></formula> Heterogeneities on the Genesis of the T-wave: A Computational Evaluation. IEEE Transactions on Biomedical Engineering. 59(2). 311–322. 52 indexed citations
8.
Keller, D. U. J., et al.. (2011). Impact of Physiological Ventricular Deformation on the Morphology of the T-Wave: A Hybrid, Static-Dynamic Approach. IEEE Transactions on Biomedical Engineering. 58(7). 2109–2119. 22 indexed citations
9.
Weber, F., D. U. J. Keller, Stefan Bauer, et al.. (2010). Predicting Tissue Conductivity Influences on Body Surface Potentials—An Efficient Approach Based on Principal Component Analysis. IEEE Transactions on Biomedical Engineering. 58(2). 265–273. 15 indexed citations
10.
Keller, D. U. J., F. Weber, Gunnar Seemann, & Olaf Dössel. (2010). Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs. IEEE Transactions on Biomedical Engineering. 57(7). 1568–1576. 99 indexed citations
11.
Seemann, Gunnar, et al.. (2010). Electrophysiological Modeling for Cardiology: Methods and Potential Applications. it - Information Technology. 52(5). 242–249. 3 indexed citations
12.
Reumann, Matthias, Blake G. Fitch, Aleksandr Rayshubskiy, et al.. (2009). Strong scaling and speedup to 16,384 processors in cardiac electro &#x2014; Mechanical simulations. 2795–2798. 17 indexed citations
13.
Reumann, Matthias, Blake G. Fitch, Aleksandr Rayshubskiy, et al.. (2009). Orthogonal recursive bisection data decomposition for high performance computing in cardiac model simulations: Dependence on anatomical geometry. PubMed. 2009. 2799–2802. 3 indexed citations
14.
Reumann, Matthias, Blake G. Fitch, Aleksandr Rayshubskiy, et al.. (2008). Large scale cardiac modeling on the Blue Gene supercomputer. PubMed. 2008. 577–580. 6 indexed citations
15.
Weber, F., D. U. J. Keller, Daniel L. Weiß, et al.. (2008). Adaptation of a minimal four-state cell model for reproducing atrial excitation properties. 275. 61–64. 11 indexed citations
16.
Reumann, Matthias, et al.. (2007). Computer Based Optimization of Biventricular Pacing According to the Left Ventricular 17 Myocardial Segments. Conference proceedings. 42. 1418–1421. 3 indexed citations
17.
18.
Weiß, Daniel L., Gunnar Seemann, D. U. J. Keller, et al.. (2007). Modeling of heterogeneous electrophysiology in the human heart with respect to ECG genesis. 49–52. 15 indexed citations
19.
Gunther, Jacob H., D. U. J. Keller, & Todd K. Moon. (2007). A Generalized BCJR Algorithm and Its Use in Iterative Blind Channel Identification. IEEE Signal Processing Letters. 14(10). 661–664. 6 indexed citations
20.
Seemann, Gunnar, D. U. J. Keller, Daniel L. Weiß, & Olaf Dössel. (2006). Modeling human ventricular geometry and fiber orientation based on diffusion tensor MRI. Computing in Cardiology Conference. 801–804. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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