Sunil B. Nagaraj

846 total citations
39 papers, 522 citations indexed

About

Sunil B. Nagaraj is a scholar working on Cognitive Neuroscience, Critical Care and Intensive Care Medicine and Anesthesiology and Pain Medicine. According to data from OpenAlex, Sunil B. Nagaraj has authored 39 papers receiving a total of 522 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cognitive Neuroscience, 11 papers in Critical Care and Intensive Care Medicine and 9 papers in Anesthesiology and Pain Medicine. Recurrent topics in Sunil B. Nagaraj's work include EEG and Brain-Computer Interfaces (20 papers), Intensive Care Unit Cognitive Disorders (11 papers) and Anesthesia and Sedative Agents (9 papers). Sunil B. Nagaraj is often cited by papers focused on EEG and Brain-Computer Interfaces (20 papers), Intensive Care Unit Cognitive Disorders (11 papers) and Anesthesia and Sedative Agents (9 papers). Sunil B. Nagaraj collaborates with scholars based in Netherlands, United States and Belgium. Sunil B. Nagaraj's co-authors include M. Brandon Westover, Michel J. A. M. van Putten, Hiddo J.L. Heerspink, Wenjun Ju, Michel Struys, Maud A. S. Weerink, Michelle J. Pena, David Zhou, Lauren McClain and Patrick L. Purdon and has published in prestigious journals such as PLoS ONE, Scientific Reports and Critical Care Medicine.

In The Last Decade

Sunil B. Nagaraj

32 papers receiving 513 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sunil B. Nagaraj Netherlands 15 187 93 75 71 70 39 522
Siddharth Biswal United States 17 239 1.3× 100 1.1× 34 0.5× 52 0.7× 59 0.8× 26 778
Woo‐Sung Kim South Korea 14 85 0.5× 66 0.7× 19 0.3× 65 0.9× 92 1.3× 44 873
Craig A. Williamson United States 16 47 0.3× 29 0.3× 34 0.5× 58 0.8× 72 1.0× 47 564
Theresa Götz Germany 13 118 0.6× 67 0.7× 34 0.5× 70 1.0× 7 0.1× 40 451
Falk von Dincklage Germany 16 115 0.6× 79 0.8× 307 4.1× 86 1.2× 27 0.4× 57 650
Bernd Walter Germany 17 42 0.2× 33 0.4× 26 0.3× 135 1.9× 141 2.0× 64 927
Behnood Gholami United States 13 24 0.1× 42 0.5× 45 0.6× 66 0.9× 31 0.4× 36 494
Juha Koskenkari Finland 14 42 0.2× 103 1.1× 20 0.3× 75 1.1× 65 0.9× 34 580
E Wang China 18 37 0.2× 219 2.4× 52 0.7× 78 1.1× 24 0.3× 73 928
Wendong Ge United States 12 200 1.1× 44 0.5× 10 0.1× 26 0.4× 38 0.5× 30 447

Countries citing papers authored by Sunil B. Nagaraj

Since Specialization
Citations

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

Fields of papers citing papers by Sunil B. Nagaraj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunil B. Nagaraj. 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 Sunil B. Nagaraj. The network helps show where Sunil B. Nagaraj may publish in the future.

Co-authorship network of co-authors of Sunil B. Nagaraj

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil B. Nagaraj. A scholar is included among the top collaborators of Sunil B. Nagaraj 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 Sunil B. Nagaraj. Sunil B. Nagaraj 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
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Radhakrishna, G., B.S. Hemanth Kumar, Sunil B. Nagaraj, et al.. (2025). Development and evaluation of S-carboxymethyl-L-cystine-loaded solid lipid nanoparticles for Parkinson’s disease in murine and zebrafish models. Scientific Reports. 15(1). 10885–10885. 5 indexed citations
3.
Nagaraj, Sunil B., et al.. (2025). Early sepsis prediction in the emergency department using machine learning. The American Journal of Emergency Medicine. 99. 143–150.
4.
Nagaraj, Sunil B., et al.. (2025). The role of artificial intelligence on supply chain resilience. Journal of Enterprise Information Management. 38(3). 950–973. 5 indexed citations
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Nagaraj, Sunil B., et al.. (2023). Effective usage of Big Data analytics in Circular Economy. 1–6.
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Rook, Mieneke, Jiali Cai, Sunil B. Nagaraj, et al.. (2022). AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation. Journal of Digital Imaging. 35(3). 538–550. 5 indexed citations
9.
Nagaraj, Sunil B., Lyanne M. Kieneker, & Michelle J. Pena. (2021). Kidney Age Index (KAI): A novel age-related biomarker to estimate kidney function in patients with diabetic kidney disease using machine learning. Computer Methods and Programs in Biomedicine. 211. 106434–106434. 6 indexed citations
10.
Pathak, Shreyasi, et al.. (2021). STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring. Artificial Intelligence in Medicine. 114. 102038–102038. 45 indexed citations
11.
Weerink, Maud A. S., et al.. (2020). Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms. Journal of Clinical Monitoring and Computing. 36(1). 121–130. 5 indexed citations
12.
Ebrahim, S, Maurice Abou Jaoude, Sunil B. Nagaraj, et al.. (2020). Accurate detection of spontaneous seizures using a generalized linear model with external validation. Epilepsia. 61(9). 1906–1918. 5 indexed citations
13.
Sun, Haoqi, Eyal Y. Kimchi, Oluwaseun Akeju, et al.. (2019). Automated tracking of level of consciousness and delirium in critical illness using deep learning. npj Digital Medicine. 2(1). 89–89. 22 indexed citations
14.
Weerink, Maud A. S., et al.. (2019). Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers. British Journal of Anaesthesia. 123(4). 479–487. 14 indexed citations
15.
Amorim, Edilberto, Sunil B. Nagaraj, Mohammad M. Ghassemi, et al.. (2019). Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury. Clinical Neurophysiology. 130(10). 1908–1916. 61 indexed citations
16.
Katheria, Anup, Sunil B. Nagaraj, Kathy Arnell, et al.. (2018). The Neu-Prem Trial: Neuromonitoring of Brains of Infants Born Preterm During Resuscitation—A Prospective Observational Cohort Study. The Journal of Pediatrics. 198. 209–213.e3. 24 indexed citations
17.
Nagaraj, Sunil B., Marleen C. Tjepkema‐Cloostermans, Barry J. Ruijter, Jeannette Hofmeijer, & Michel J. A. M. van Putten. (2018). The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest. Clinical Neurophysiology. 129(12). 2557–2566. 30 indexed citations
18.
Nagaraj, Sunil B., et al.. (2017). ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit. Journal of Clinical Monitoring and Computing. 32(1). 53–61. 7 indexed citations
19.
Nagaraj, Sunil B., Nathan J. Stevenson, William P. Marnane, Geraldine B. Boylan, & Gordon Lightbody. (2012). A novel dictionary for neonatal EEG seizure detection using atomic decomposition. PubMed. 53. 1073–1076. 4 indexed citations
20.

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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