Xiaorong Ding

3.7k total citations · 2 hit papers
68 papers, 2.4k citations indexed

About

Xiaorong Ding is a scholar working on Biomedical Engineering, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Xiaorong Ding has authored 68 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Biomedical Engineering, 39 papers in Cardiology and Cardiovascular Medicine and 19 papers in Surgery. Recurrent topics in Xiaorong Ding's work include Non-Invasive Vital Sign Monitoring (38 papers), Heart Rate Variability and Autonomic Control (30 papers) and Hemodynamic Monitoring and Therapy (19 papers). Xiaorong Ding is often cited by papers focused on Non-Invasive Vital Sign Monitoring (38 papers), Heart Rate Variability and Autonomic Control (30 papers) and Hemodynamic Monitoring and Therapy (19 papers). Xiaorong Ding collaborates with scholars based in China, Hong Kong and United Kingdom. Xiaorong Ding's co-authors include Yuan‐Ting Zhang, Ni Zhao, Jing Liu, Hon Ki Tsang, Benny Lo, Guang‐Zhong Yang, Wenxuan Dai, Yali Zheng, Heye Zhang and Carmen C. Y. Poon and has published in prestigious journals such as PLoS ONE, Advanced Functional Materials and Scientific Reports.

In The Last Decade

Xiaorong Ding

63 papers receiving 2.3k citations

Hit Papers

Unobtrusive Sensing and Wearable Devices for Health Infor... 2014 2026 2018 2022 2014 2023 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaorong Ding China 19 1.7k 1.2k 761 221 202 68 2.4k
Dingchang Zheng United Kingdom 28 1.5k 0.9× 1.7k 1.4× 740 1.0× 105 0.5× 319 1.6× 177 2.9k
Marian Walter Germany 22 1.2k 0.7× 610 0.5× 534 0.7× 403 1.8× 59 0.3× 162 2.1k
Tapas Mondal Canada 26 1.2k 0.7× 724 0.6× 421 0.6× 506 2.3× 112 0.6× 103 3.6k
Peter Charlton United Kingdom 24 1.8k 1.1× 1.5k 1.3× 871 1.1× 106 0.5× 267 1.3× 89 3.2k
Xiao Hu United States 33 1.2k 0.7× 1.3k 1.1× 1.0k 1.3× 98 0.4× 414 2.0× 209 3.7k
Masaki Sekine Japan 19 1.1k 0.7× 703 0.6× 566 0.7× 85 0.4× 114 0.6× 65 1.9k
Antti Vehkaoja Finland 21 843 0.5× 583 0.5× 254 0.3× 156 0.7× 64 0.3× 120 1.4k
Mario Cesarelli Italy 34 1.5k 0.9× 723 0.6× 342 0.4× 161 0.7× 317 1.6× 259 3.9k
James E. Voos United States 34 922 0.6× 431 0.4× 2.7k 3.5× 151 0.7× 109 0.5× 144 4.2k
Gaetano D. Gargiulo Australia 25 1.3k 0.8× 499 0.4× 281 0.4× 140 0.6× 56 0.3× 104 2.0k

Countries citing papers authored by Xiaorong Ding

Since Specialization
Citations

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

Fields of papers citing papers by Xiaorong Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaorong Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaorong Ding. A scholar is included among the top collaborators of Xiaorong Ding 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 Xiaorong Ding. Xiaorong Ding 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
3.
Chakraborti, Tapabrata, et al.. (2024). Uncertainty quantification of cuffless blood pressure estimation based on parameterized model evidential ensemble learning. Biomedical Signal Processing and Control. 92. 106104–106104. 6 indexed citations
4.
Lu, Huiqi, et al.. (2024). CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation. IEEE Journal of Biomedical and Health Informatics. 28(5). 2674–2686. 6 indexed citations
5.
Chen, Yifan, et al.. (2024). Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms. Sensors. 24(7). 2248–2248. 12 indexed citations
6.
Chen, Yifan, et al.. (2024). Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure. Reviews in Cardiovascular Medicine. 25(10). 386–386. 8 indexed citations
7.
Yin, Chongbo, Yan Shi, Xiaorong Ding, & Xingming Guo. (2023). Lightweight network coupled with heart sounds to evaluate exercise-induced cardiac fatigue. Sensors and Actuators A Physical. 358. 114458–114458.
8.
Zhang, Yuan‐Ting, et al.. (2023). Causal inference based cuffless blood pressure estimation: A pilot study. Computers in Biology and Medicine. 159. 106900–106900. 9 indexed citations
9.
Li, Ying, et al.. (2023). Risk factors for falls among community-dwelling older adults: A systematic review and meta-analysis. Frontiers in Medicine. 9. 1019094–1019094. 55 indexed citations breakdown →
10.
Zhang, Chunlin, et al.. (2023). Continual Learning for Cuffless Blood Pressure Measurement using PPG and ECG Signals. PubMed. 2023. 1–4. 1 indexed citations
11.
Ding, Xiaorong, et al.. (2022). Wearable Vital Signs Monitoring for Patients With Asthma: A Review. IEEE Sensors Journal. 23(3). 1734–1751. 16 indexed citations
12.
Ding, Xiaorong, David A. Clifton, Nan Ji, et al.. (2020). Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic. IEEE Reviews in Biomedical Engineering. 14. 48–70. 186 indexed citations
13.
Ding, Xiaorong, et al.. (2019). Investigation on the status quo and willingness of Shenzhen hemodialysis nurses' use of indwelling needle in dialysis. Zhonghua xiandai huli zazhi. 25(33). 4296–4300. 1 indexed citations
14.
Miao, Fen, Nan Fu, Yuan‐Ting Zhang, et al.. (2017). A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques. IEEE Journal of Biomedical and Health Informatics. 21(6). 1730–1740. 127 indexed citations
15.
Ding, Xiaorong, et al.. (2017). Learning to Predict Blood Pressure with Deep Bidirectional LSTM Network.. arXiv (Cornell University). 5 indexed citations
16.
Ding, Xiaorong, et al.. (2016). Research and construction of core competence evaluation index system for new nurses. ˜The œJournal of practical nursing. 32(22). 1681–1684. 1 indexed citations
17.
Ding, Xiaorong, Yuan‐Ting Zhang, & Hon Ki Tsang. (2016). Impact of heart disease and calibration interval on accuracy of pulse transit time–based blood pressure estimation. Physiological Measurement. 37(2). 227–237. 41 indexed citations
18.
Liu, Jing, Yao Li, Xiaorong Ding, Wenxuan Dai, & Yuan‐Ting Zhang. (2015). Effects of cuff inflation and deflation on pulse transit time measured from ECG and multi-wavelength PPG. PubMed. 2015. 5973–5976. 13 indexed citations
19.
Ding, Xiaorong. (2012). Heart sound recognition based on EMD approximate entropy and SVM. Zhendong yu chongji. 1 indexed citations
20.
Yan, Shiyan, Chen Yao, Xiaorong Ding, Miao Jiang, & Aiping Lü. (2011). Serum low-density lipoprotein and heart rate related to the effectiveness in the treatment of angina pectoris with two similar Chinese medicine herbal formulae. Chinese Journal of Integrative Medicine. 17(12). 903–907. 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.

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