Danny Eytan

2.1k total citations
49 papers, 1.2k citations indexed

About

Danny Eytan is a scholar working on Cognitive Neuroscience, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Danny Eytan has authored 49 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cognitive Neuroscience, 15 papers in Cardiology and Cardiovascular Medicine and 14 papers in Surgery. Recurrent topics in Danny Eytan's work include Neural dynamics and brain function (10 papers), Non-Invasive Vital Sign Monitoring (9 papers) and ECG Monitoring and Analysis (8 papers). Danny Eytan is often cited by papers focused on Neural dynamics and brain function (10 papers), Non-Invasive Vital Sign Monitoring (9 papers) and ECG Monitoring and Analysis (8 papers). Danny Eytan collaborates with scholars based in Israel, Canada and United States. Danny Eytan's co-authors include Shimon Marom, Naama Brenner, A. Gal, Avner Wallach, Mjaye Mazwi, Peter C. Laussen, Andrew Goodwin, Sebastian D. Goodfellow, Robert Greer and Christoph Zrenner and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Danny Eytan

47 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Danny Eytan Israel 17 745 649 213 153 145 49 1.2k
Theodoros P. Zanos United States 19 618 0.8× 450 0.7× 67 0.3× 101 0.7× 173 1.2× 58 1.3k
Ahmet Omurtag United States 19 888 1.2× 235 0.4× 102 0.5× 213 1.4× 101 0.7× 46 1.2k
Carl A. Gold United States 16 646 0.9× 455 0.7× 110 0.5× 47 0.3× 14 0.1× 56 1.3k
Steven L. Weinstein United States 23 586 0.8× 425 0.7× 28 0.1× 58 0.4× 77 0.5× 40 1.9k
Peter N. Taylor United Kingdom 24 1.3k 1.7× 423 0.7× 31 0.1× 84 0.5× 17 0.1× 86 1.7k
J.-F. Vibert France 18 371 0.5× 172 0.3× 65 0.3× 36 0.2× 56 0.4× 44 920
Jin Jing United States 21 756 1.0× 202 0.3× 63 0.3× 58 0.4× 293 2.0× 100 1.6k
Mark L. Scheuer United States 19 753 1.0× 333 0.5× 18 0.1× 65 0.4× 105 0.7× 36 1.7k
Madeline Fields United States 18 453 0.6× 221 0.3× 32 0.2× 28 0.2× 95 0.7× 51 985
Erik Edwards United States 26 1.2k 1.6× 192 0.3× 38 0.2× 64 0.4× 43 0.3× 46 2.9k

Countries citing papers authored by Danny Eytan

Since Specialization
Citations

This map shows the geographic impact of Danny Eytan'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 Danny Eytan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danny Eytan more than expected).

Fields of papers citing papers by Danny Eytan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danny Eytan. 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 Danny Eytan. The network helps show where Danny Eytan may publish in the future.

Co-authorship network of co-authors of Danny Eytan

This figure shows the co-authorship network connecting the top 25 collaborators of Danny Eytan. A scholar is included among the top collaborators of Danny Eytan 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 Danny Eytan. Danny Eytan 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.
Goodwin, Andrew, William G Dixon, Mjaye Mazwi, et al.. (2023). The truth Hertz—synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit. Physiological Measurement. 44(8). 85002–85002. 1 indexed citations
2.
Sobel, Jonathan, Jeremy Levy, Ronit Almog, et al.. (2023). Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients. Scientific Reports. 13(1). 442–442. 10 indexed citations
3.
Joshi, Shalmali, et al.. (2023). Making machine learning matter to clinicians: model actionability in medical decision-making. npj Digital Medicine. 6(1). 7–7. 30 indexed citations
4.
Mazwi, Mjaye, et al.. (2023). iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside. PLoS Computational Biology. 19(9). e1010835–e1010835.
5.
Hahn, Cecil D., Thomas De Cooman, Sabine Van Huffel, et al.. (2022). Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit. arXiv (Cornell University). 4 indexed citations
6.
Amir, Ofra, et al.. (2022). Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. Journal of Biomedical Informatics. 132. 104107–104107. 7 indexed citations
7.
Borenstein‐Levin, Liron, Ori Hochwald, Josef Ben‐Ari, et al.. (2022). Same baby, different care: variations in practice between neonatologists and pediatric intensivists. European Journal of Pediatrics. 181(4). 1669–1677. 13 indexed citations
8.
Jegatheeswaran, Anusha, Sebastian D. Goodfellow, William G Dixon, et al.. (2022). Heart rate variability is markedly abnormal following surgical repair of atrial and ventricular septal defects in pediatric patients. SHILAP Revista de lepidopterología. 7. 100333–100333. 1 indexed citations
9.
Somer, Jonathan, Yaron Bar‐Lavie, Arnona Ziv, et al.. (2021). Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study. Journal of the American Medical Informatics Association. 28(6). 1188–1196. 24 indexed citations
10.
Eytan, Danny, et al.. (2020). Blood Pressure Estimation From PPG Signals Using Convolutional Neural Networks And Siamese Network. 1135–1139. 48 indexed citations
11.
Goodwin, Andrew, Danny Eytan, Mjaye Mazwi, et al.. (2020). A practical approach to storage and retrieval of high-frequency physiological signals. Physiological Measurement. 41(3). 35008–35008. 16 indexed citations
12.
Yair, Or, et al.. (2019). Domain Adaptation Using Riemannian Geometry of Spd Matrices. 4464–4468. 5 indexed citations
13.
Goodfellow, Sebastian D., Andrew Goodwin, Robert Greer, et al.. (2018). Towards Understanding ECG Rhythm Classification Using Convolutional Neural Networks and Attention Mappings. 83–101. 31 indexed citations
14.
Tonekaboni, Sana, Mjaye Mazwi, Peter C. Laussen, et al.. (2018). Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU. 534–550. 15 indexed citations
15.
Eytan, Danny, et al.. (2018). VV extracorporeal life support for the Third Millennium: will we need anticoagulation?. Journal of Thoracic Disease. 10(S5). S698–S706. 6 indexed citations
16.
Kermany, Einat, et al.. (2010). Tradeoffs and Constraints on Neural Representation in Networks of Cortical Neurons. Journal of Neuroscience. 30(28). 9588–9596. 29 indexed citations
17.
Zrenner, Christoph, Danny Eytan, Avner Wallach, Peter Thier, & Shimon Marom. (2010). A Generic Framework for Real-Time Multi-Channel Neuronal Signal Analysis, Telemetry Control, and Sub-Millisecond Latency Feedback Generation. Frontiers in Neuroscience. 4. 173–173. 27 indexed citations
18.
Eytan, Danny & Shimon Marom. (2006). Dynamics and Effective Topology Underlying Synchronization in Networks of Cortical Neurons. Journal of Neuroscience. 26(33). 8465–8476. 300 indexed citations
19.
Eytan, Danny, Amir Minerbi, Noam Ziv, & Shimon Marom. (2004). Dopamine-Induced Dispersion of Correlations Between Action Potentials in Networks of Cortical Neurons. Journal of Neurophysiology. 92(3). 1817–1824. 51 indexed citations
20.
Marom, Shimon & Danny Eytan. (2004). Learning in ex-vivo developing networks of cortical neurons. Progress in brain research. 147. 189–199. 36 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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