In Cheol Jeong

1.2k total citations
71 papers, 829 citations indexed

About

In Cheol Jeong is a scholar working on Cardiology and Cardiovascular Medicine, Biomedical Engineering and Surgery. According to data from OpenAlex, In Cheol Jeong has authored 71 papers receiving a total of 829 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cardiology and Cardiovascular Medicine, 24 papers in Biomedical Engineering and 12 papers in Surgery. Recurrent topics in In Cheol Jeong's work include Non-Invasive Vital Sign Monitoring (20 papers), Heart Rate Variability and Autonomic Control (14 papers) and ECG Monitoring and Analysis (9 papers). In Cheol Jeong is often cited by papers focused on Non-Invasive Vital Sign Monitoring (20 papers), Heart Rate Variability and Autonomic Control (14 papers) and ECG Monitoring and Analysis (9 papers). In Cheol Jeong collaborates with scholars based in United States, South Korea and Japan. In Cheol Jeong's co-authors include Joseph Finkelstein, Peter C. Searson, Dong‐Hoon Choi, Garry R. Cutting, Patrick R. Sosnay, Dong Hee Lee, Sung Oh Hwang, Semin Ryu, Jeong L. Sohn and Seung Jin Song and has published in prestigious journals such as PLoS ONE, Scientific Reports and Biochemical and Biophysical Research Communications.

In The Last Decade

In Cheol Jeong

63 papers receiving 812 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
In Cheol Jeong United States 13 363 270 146 110 68 71 829
Brinnae Bent United States 15 525 1.4× 350 1.3× 106 0.7× 117 1.1× 137 2.0× 30 1.3k
Ryan Runge United States 6 358 1.0× 215 0.8× 74 0.5× 102 0.9× 123 1.8× 8 976
Duarte Dias Portugal 7 339 0.9× 197 0.7× 69 0.5× 50 0.5× 64 0.9× 23 746
Maurizio Ferratini Italy 16 316 0.9× 308 1.1× 120 0.8× 87 0.8× 39 0.6× 38 758
Kari Antila Finland 21 276 0.8× 633 2.3× 126 0.9× 87 0.8× 31 0.5× 58 1.1k
Christopher G. Scully United States 15 491 1.4× 501 1.9× 264 1.8× 79 0.7× 21 0.3× 61 1.0k
Ebrahim Nemati United States 17 481 1.3× 241 0.9× 77 0.5× 152 1.4× 58 0.9× 62 1.1k
Zhengbo Zhang China 17 337 0.9× 325 1.2× 102 0.7× 64 0.6× 22 0.3× 106 870
Ainara Garde Canada 19 510 1.4× 390 1.4× 181 1.2× 324 2.9× 83 1.2× 55 1.0k
Mohamad Forouzanfar Canada 20 537 1.5× 545 2.0× 217 1.5× 109 1.0× 20 0.3× 70 1.4k

Countries citing papers authored by In Cheol Jeong

Since Specialization
Citations

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

Fields of papers citing papers by In Cheol Jeong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by In Cheol Jeong. 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 In Cheol Jeong. The network helps show where In Cheol Jeong may publish in the future.

Co-authorship network of co-authors of In Cheol Jeong

This figure shows the co-authorship network connecting the top 25 collaborators of In Cheol Jeong. A scholar is included among the top collaborators of In Cheol Jeong 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 In Cheol Jeong. In Cheol Jeong 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.
Ryu, Semin, et al.. (2025). Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation. Scientific Reports. 15(1). 21592–21592. 2 indexed citations
2.
Ryu, Semin, et al.. (2024). Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks. Sensors. 24(5). 1610–1610. 4 indexed citations
3.
Song, Young‐Jin, et al.. (2024). GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods. Kidney Research and Clinical Practice. 44(5). 788–801. 1 indexed citations
4.
Ryu, Semin, et al.. (2024). ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks. PubMed. 2024. 1–6. 1 indexed citations
5.
Moon, Hyo Youl & In Cheol Jeong. (2023). The effect of voluntary exercise on light cycle stress-induced metabolic resistance. PubMed. 27(3). 1–9. 1 indexed citations
6.
Ryu, Semin, et al.. (2022). iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform. Frontiers in Physiology. 13. 825612–825612. 6 indexed citations
7.
Jeong, In Cheol, et al.. (2020). Association Between System Usage Pattern and Impact of Web-Based Telerehabilitation in Patients with Multiple Sclerosis. Studies in health technology and informatics. 272. 346–349. 6 indexed citations
8.
Brody, Rachel, et al.. (2020). Towards a Highly Usable, Mobile Electronic Platform for Patient Recruitment and Consent Management. Studies in health technology and informatics. 270. 1066–1070. 8 indexed citations
9.
Jeong, In Cheol, Herb Karpatkin, Joel Stein, & Joseph Finkelstein. (2020). Relationship Between Exercise Duration in Multimodal Telerehabilitation and Quality of Sleep in Patients with Multiple Sclerosis. Studies in health technology and informatics. 270. 658–662. 11 indexed citations
10.
Jeong, In Cheol, et al.. (2019). Mining Electronic Dental Records to Identify Dry Socket Risk Factors. Studies in health technology and informatics. 262. 328–331. 8 indexed citations
11.
Jeong, In Cheol, et al.. (2019). Using Big Data to Uncover Patient Determinants of Care Utilization Compliance in a Student Dental Clinic. Studies in health technology and informatics. 262. 324–327. 1 indexed citations
12.
Choi, Dong‐Hoon, et al.. (2018). Sweat test for cystic fibrosis: Wearable sweat sensor vs. standard laboratory test. Journal of Cystic Fibrosis. 17(4). e35–e38. 60 indexed citations
13.
Jeong, In Cheol, et al.. (2017). Development of Search and Rescue System with Dynamic Model by RF Signal Based LTE. 12(4). 120–124. 1 indexed citations
14.
Finkelstein, Joseph & In Cheol Jeong. (2016). Using CART for advanced prediction of asthma attacks based on telemonitoring data. 1–5. 10 indexed citations
15.
Finkelstein, Joseph & In Cheol Jeong. (2013). Feasibility of Interactive Biking Exercise System for Telemanagement in Elderly. Studies in health technology and informatics. 192. 642–6. 12 indexed citations
16.
Jeong, In Cheol & Joseph Finkelstein. (2013). Optimizing Non-Invasive Blood Pressure Estimation Using Pulse Transit Time. Studies in health technology and informatics. 192. 1198–1198. 4 indexed citations
17.
Jeong, In Cheol, et al.. (2009). Two cases of small bowel herniation through 5 mm trocar site following removal of drains after gynecologic laparoscopy. Obstetrics & Gynecology Science. 52(1). 129–132.
18.
Jeong, In Cheol, et al.. (2007). Compensation of Error in Noninvasive Blood Pressure Measurement System Using Optical Sensor. Journal of Biomedical Engineering Research. 28(2). 178–186. 1 indexed citations
19.
Jeong, In Cheol, et al.. (2006). A new method to estimate arterial blood pressure using photoplethysmographic signal. PubMed. 2006. 4667–4670. 20 indexed citations
20.
Jeong, In Cheol, et al.. (2001). Regulation of leptin gene expression by insulin and growth hormone in mouse adipocytes. Experimental & Molecular Medicine. 33(4). 234–239. 16 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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