Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology
202514 citationsJosé Millet et al.EP Europaceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of José Millet'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 José Millet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José Millet more than expected).
This network shows the impact of papers produced by José Millet. 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 José Millet. The network helps show where José Millet may publish in the future.
Co-authorship network of co-authors of José Millet
This figure shows the co-authorship network connecting the top 25 collaborators of José Millet.
A scholar is included among the top collaborators of José Millet 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 José Millet. José Millet is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Climent, Andreu M., Alejandro Liberos, Miguel Rodrigo, et al.. (2014). Accuracy of inverse solution computation of dominant frequencies and phases during atrial fibrillation. Computing in Cardiology. 41. 537–540.2 indexed citations
11.
Rodrigo, Miguel, Andreu M. Climent, Alejandro Liberos, et al.. (2014). Non-invasive detection of reentrant drivers during atrial fibrillation: A clinical-computational study. Computing in Cardiology Conference. 41. 9–12.1 indexed citations
12.
Moreno, Javier, et al.. (2014). Frequency spectrum correlation along atria to study atrial fibrillation recurrence. Computing in Cardiology. 1125–1128.3 indexed citations
13.
Liberos, Alejandro, et al.. (2013). Accuracy of non-invasive frequency estimation during atrial fibrillation. Computing in Cardiology Conference. 1183–1186.
14.
Liberos, Alejandro, et al.. (2013). Body surface potential propagation maps during macroreentrant atrial arrhythmias. A simulation study. Computing in Cardiology. 915–918.1 indexed citations
15.
Moreno, Javier, et al.. (2013). Hurst exponent for the analysis of atrial fibrillation recurrence after ablation procedures. Computing in Cardiology Conference. 1115–1118.1 indexed citations
16.
Climent, Andreu M., Alejandro Liberos, Esther Pérez David, et al.. (2012). Non-invasive estimation of the activation sequence in the atria during sinus rhythm and atrial tachyarrhythmia. Computing in Cardiology. 901–904.4 indexed citations
17.
Rodrigo, Miguel, Alejandro Liberos, María S. Guillem, José Millet, & Andreu M. Climent. (2011). Causality relation map: A novel methodology for the identification of hierarchical fibrillatory processes. Computing in Cardiology. 173–176.5 indexed citations
18.
Climent, Andreu M., José Millet, Paola Berne, et al.. (2011). Fragmentation in Body Surface Potential Mapping recordings from patients with Brugada syndrome. Computing in Cardiology. 637–640.2 indexed citations
19.
Climent, Andreu M., et al.. (2010). An iterative method for indirectly solving the inverse problem of electrocardiography. Computing in Cardiology. 89–92.1 indexed citations
20.
Millet, José, et al.. (2006). Contributions for the optimal lead placement for the study of atrial fibrillation applying Independent Component Analysis to 64 body surface potential mapping recordings. Computing in Cardiology Conference. 389–392.2 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.