Deepu John
Impact in
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- Artificial Intelligence in Healthcare
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- ECG Monitoring and Analysis
Papers in
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- ECG Monitoring and Analysis 36
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- EEG and Brain-Computer Interfaces 22
- Co-authors
- Barry Cardiff (37 shared papers)Soumyabrata Dev (4 shared papers)Chun-Huat Heng (5 shared papers)Yong Lian (5 shared papers)Bharadwaj Veeravalli (2 shared papers)Hewei Wang (1 shared paper)Rajesh Chandrasekhara Panicker (10 shared papers)Xiaoyang Zhang (1 shared paper)
In The Last Decade
Deepu John
64 papers receiving 872 citations
Deepu John's Hit Papers
Peers
Comparison fields: 5 of 84
- Health Information Management 125
- Cardiology and Cardiovascular Medicine 367
- Cognitive Neuroscience 210
- Biomedical Engineering 330
- Neurology 55
Countries citing papers authored by Deepu John
This map shows the geographic impact of Deepu John'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 Deepu John with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepu John more than expected).
Fields of papers citing papers by Deepu John
This network shows the impact of papers produced by Deepu John. 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 Deepu John. The network helps show where Deepu John may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepu John, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A predictive analytics approach for stroke prediction using machine learning and neural networks Hit paper breakdown → | 2022 | 160 |
| 2 | 2022 | 115 | |
| 3 | 2016 | 72 | |
| 4 | 2021 | 46 | |
| 5 | 2020 | 34 | |
| 6 | 2021 | 33 | |
| 7 | 2021 | 32 | |
| 8 | 2020 | 29 | |
| 9 | 2022 | 29 | |
| 10 | 2018 | 26 | |
| 11 | 2020 | 25 | |
| 12 | 2023 | 20 | |
| 13 | 2021 | 18 | |
| 14 | 2020 | 17 | |
| 15 | 2022 | 16 | |
| 16 | 2021 | 16 | |
| 17 | 2023 | 16 | |
| 18 | 2021 | 15 | |
| 19 | 2024 | 14 | |
| 20 | 2022 | 14 |
About Deepu John
Deepu John is a scholar working on Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Biomedical Engineering, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 68 papers that have together received 919 indexed citations. Recurring topics across this work include ECG Monitoring and Analysis (36 papers), EEG and Brain-Computer Interfaces (22 papers), Non-Invasive Vital Sign Monitoring (20 papers), Analog and Mixed-Signal Circuit Design (12 papers), Advanced Neural Network Applications (5 papers), Hemodynamic Monitoring and Therapy (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Advanced Memory and Neural Computing (4 papers). The work is most often cited by research in Health Information Management (125 citations), Cardiology and Cardiovascular Medicine (367 citations), Cognitive Neuroscience (210 citations), Biomedical Engineering (330 citations) and Neurology (55 citations). Deepu John has collaborated with scholars based in Ireland, Singapore and France. Frequent co-authors include Barry Cardiff, Soumyabrata Dev, Chun-Huat Heng, Yong Lian, Bharadwaj Veeravalli, Hewei Wang, Rajesh Chandrasekhara Panicker, Xiaoyang Zhang, David Liang Tai Wong and Avishek Nag. Their work appears in journals such as IEEE Transactions on Biomedical Circuits and Systems, Information Fusion, IEEE Transactions on Circuits & Systems II Express Briefs, IEEE Access and IEEE Sensors Journal.
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.