Jen Hong Tan

13.9k total citations · 8 hit papers
102 papers, 10.4k citations indexed

About

Jen Hong Tan is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Cognitive Neuroscience. According to data from OpenAlex, Jen Hong Tan has authored 102 papers receiving a total of 10.4k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Radiology, Nuclear Medicine and Imaging, 29 papers in Cardiology and Cardiovascular Medicine and 28 papers in Cognitive Neuroscience. Recurrent topics in Jen Hong Tan's work include ECG Monitoring and Analysis (27 papers), EEG and Brain-Computer Interfaces (26 papers) and Retinal Imaging and Analysis (19 papers). Jen Hong Tan is often cited by papers focused on ECG Monitoring and Analysis (27 papers), EEG and Brain-Computer Interfaces (26 papers) and Retinal Imaging and Analysis (19 papers). Jen Hong Tan collaborates with scholars based in Singapore, Malaysia and India. Jen Hong Tan's co-authors include U. Rajendra Acharya, Yuki Hagiwara, Shu Lih Oh, Muhammad Adam, Hamido Fujita, Hojjat Adeli, E. Y. K. Ng, Ru‐San Tan, Oh Shu Lih and Sulatha V. Bhandary and has published in prestigious journals such as Journal of Lipid Research, Information Sciences and Journal of Medical Internet Research.

In The Last Decade

Jen Hong Tan

98 papers receiving 10.1k citations

Hit Papers

Deep convolutional neural... 2017 2026 2020 2023 2017 2017 2018 2017 2017 250 500 750 1000
What are hit papers?

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.

  1. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
    2017 1.2k citations U. Rajendra Acharya, Shu Lih Oh et al. Computers in Biology and Medicine profile →
  2. A deep convolutional neural network model to classify heartbeats
    2017 987 citations U. Rajendra Acharya, Shu Lih Oh et al. Computers in Biology and Medicine profile →
  3. Deep learning for healthcare applications based on physiological signals: A review
    2018 680 citations Oliver Faust, Yuki Hagiwara et al. Computer Methods and Programs in Biomedicine profile →
  4. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
    2017 678 citations U. Rajendra Acharya, Hamido Fujita et al. Information Sciences profile →
  5. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
    2017 557 citations U. Rajendra Acharya, Hamido Fujita et al. Information Sciences profile →
  6. Automated EEG-based screening of depression using deep convolutional neural network
    2018 450 citations U. Rajendra Acharya, Shu Lih Oh et al. Computer Methods and Programs in Biomedicine profile →
  7. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
    2018 336 citations U. Raghavendra, Hamido Fujita et al. Information Sciences profile →
  8. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals
    2018 289 citations Jen Hong Tan, Yuki Hagiwara et al. Computers in Biology and Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jen Hong Tan Singapore 47 4.4k 4.3k 2.9k 1.8k 1.3k 102 10.4k
Yuki Hagiwara Singapore 33 4.1k 0.9× 3.7k 0.8× 1.2k 0.4× 1.5k 0.8× 1.0k 0.8× 68 8.1k
Ram Bilas Pachori India 66 7.7k 1.8× 3.6k 0.8× 1.6k 0.6× 1.8k 1.0× 1.9k 1.5× 298 13.3k
Jasjit S. Suri United States 61 2.0k 0.5× 6.2k 1.4× 4.8k 1.7× 2.1k 1.1× 2.2k 1.7× 436 15.2k
Oliver Faust Singapore 41 2.4k 0.6× 2.1k 0.5× 1.1k 0.4× 1.1k 0.6× 842 0.7× 123 5.8k
Ru‐San Tan Singapore 56 3.6k 0.8× 8.3k 1.9× 2.4k 0.8× 2.3k 1.3× 1.3k 1.0× 400 13.5k
Roshan Joy Martis India 32 2.6k 0.6× 2.6k 0.6× 573 0.2× 902 0.5× 492 0.4× 72 4.4k
S. Vinitha Sree Singapore 32 1.4k 0.3× 1.0k 0.2× 1.4k 0.5× 428 0.2× 663 0.5× 56 4.0k
Filippo Molinari Italy 47 1.1k 0.2× 2.1k 0.5× 2.7k 0.9× 1.3k 0.7× 1.6k 1.2× 311 8.5k
Edward J. Ciaccio United States 41 1.3k 0.3× 2.5k 0.6× 851 0.3× 650 0.4× 650 0.5× 219 6.1k
Choo Min Lim Singapore 20 1.5k 0.3× 2.2k 0.5× 731 0.3× 1.1k 0.6× 261 0.2× 29 4.3k

Countries citing papers authored by Jen Hong Tan

Since Specialization
Citations

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

Fields of papers citing papers by Jen Hong Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jen Hong Tan. 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 Jen Hong Tan. The network helps show where Jen Hong Tan may publish in the future.

Co-authorship network of co-authors of Jen Hong Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Jen Hong Tan. A scholar is included among the top collaborators of Jen Hong Tan 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 Jen Hong Tan. Jen Hong Tan 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.
Lim, Daniel Yan Zheng, Yuhe Ke, Jen Hong Tan, et al.. (2025). Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score. BMJ Open Gastroenterology. 12(1). e001496–e001496. 1 indexed citations
2.
Salvi, Massimo, et al.. (2025). Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 15(1). 3 indexed citations
3.
Faust, Oliver, Yuki Hagiwara, Jen Hong Tan, Oh Shu Lih, & U. Rajendra Acharya. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine. 161. 1–13. 680 indexed citations breakdown →
4.
Adam, Muhammad, Shu Lih Oh, Vidya K. Sudarshan, et al.. (2018). Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals. Computer Methods and Programs in Biomedicine. 161. 133–143. 43 indexed citations
5.
Raghavendra, U., Hamido Fujita, Sulatha V. Bhandary, et al.. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences. 441. 41–49. 336 indexed citations breakdown →
6.
Tong, Louis, Hla Myint Htoon, Aihua Hou, et al.. (2018). Acupuncture and herbal formulation compared with artificial tears alone: evaluation of dry eye symptoms and associated tests in randomised clinical trial. BMJ Open Ophthalmology. 3(1). e000150–e000150. 14 indexed citations
7.
Hagiwara, Yuki, Hamido Fujita, Shu Lih Oh, et al.. (2018). Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review. Information Sciences. 467. 99–114. 151 indexed citations
8.
Acharya, U. Rajendra, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, & Hojjat Adeli. (2017). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine. 100. 270–278. 1167 indexed citations breakdown →
9.
Acharya, U. Rajendra, Hamido Fujita, Shu Lih Oh, et al.. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences. 415-416. 190–198. 678 indexed citations breakdown →
10.
Koh, Joel E.W., U. Rajendra Acharya, Yuki Hagiwara, et al.. (2017). Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies. Computers in Biology and Medicine. 84. 89–97. 68 indexed citations
11.
Sudarshan, Vidya K., U. Rajendra Acharya, Shu Lih Oh, et al.. (2017). Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2 s of ECG signals. Computers in Biology and Medicine. 83. 48–58. 67 indexed citations
12.
Zhao, Yang, et al.. (2016). Clinical Trial of Thermal Pulsation (LipiFlow) in Meibomian Gland Dysfunction With Preteatment Meibography. Eye & Contact Lens Science & Clinical Practice. 42(6). 339–346. 56 indexed citations
13.
14.
Acharya, U. Rajendra, Hamido Fujita, Muhammad Adam, et al.. (2016). Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study. Information Sciences. 377. 17–29. 193 indexed citations
15.
Acharya, U. Rajendra, Muthu Rama Krishnan Mookiah, Joel E.W. Koh, et al.. (2016). Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Computers in Biology and Medicine. 75. 54–62. 61 indexed citations
16.
Mookiah, Muthu Rama Krishnan, U. Rajendra Acharya, Hamido Fujita, et al.. (2015). Local configuration pattern features for age-related macular degeneration characterization and classification. Computers in Biology and Medicine. 63. 208–218. 45 indexed citations
17.
Tong, Louis, Sin Man Lam, Guanghou Shui, et al.. (2014). Longitudinal changes of tear lipids with lid warming treatment in meibomian gland dysfunction. Investigative Ophthalmology & Visual Science. 55(13). 34–34. 1 indexed citations
18.
Tan, Jen Hong, U. Rajendra Acharya, Hwee Kuan Lee, et al.. (2014). Thermal pulsation for meibomian gland dysfunction in Asian patients. Investigative Ophthalmology & Visual Science. 55(13). 46–46. 1 indexed citations
19.
Tan, Jen Hong, E. Y. K. Ng, & U. Rajendra Acharya. (2010). Evaluation of tear evaporation from ocular surface by functional infrared thermography. Medical Physics. 37(11). 6022–6034. 55 indexed citations
20.
Tan, Jen Hong, E. Y. K. Ng, U. Rajendra Acharya, & C Chee. (2010). Automated study of ocular thermal images: Comprehensive analysis of corneal health with different age group subjects and validation. Digital Signal Processing. 20(6). 1579–1591. 20 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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