Shivnarayan Patidar

1.5k total citations
28 papers, 1.1k citations indexed

About

Shivnarayan Patidar is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Cognitive Neuroscience. According to data from OpenAlex, Shivnarayan Patidar has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cardiology and Cardiovascular Medicine, 10 papers in Pulmonary and Respiratory Medicine and 8 papers in Cognitive Neuroscience. Recurrent topics in Shivnarayan Patidar's work include ECG Monitoring and Analysis (16 papers), Phonocardiography and Auscultation Techniques (10 papers) and EEG and Brain-Computer Interfaces (8 papers). Shivnarayan Patidar is often cited by papers focused on ECG Monitoring and Analysis (16 papers), Phonocardiography and Auscultation Techniques (10 papers) and EEG and Brain-Computer Interfaces (8 papers). Shivnarayan Patidar collaborates with scholars based in India, Singapore and United States. Shivnarayan Patidar's co-authors include Ram Bilas Pachori, U. Rajendra Acharya, Trilochan Panigrahi, Abhay Upadhyay, Ashish Sharma, Soumya Jana, Ru‐San Tan, Vaneet Aggarwal, T. Veerakumar and M. H. Vasantha and has published in prestigious journals such as Critical Care Medicine, Expert Systems with Applications and Applied Soft Computing.

In The Last Decade

Shivnarayan Patidar

27 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shivnarayan Patidar India 14 656 503 405 204 195 28 1.1k
Yakup Kutlu Türkiye 15 352 0.5× 333 0.7× 199 0.5× 286 1.4× 159 0.8× 60 978
Zümray Dokur Türkiye 19 444 0.7× 425 0.8× 332 0.8× 347 1.7× 198 1.0× 58 1.3k
Reinhold Orglmeister Germany 15 576 0.9× 1.0k 2.0× 485 1.2× 159 0.8× 839 4.3× 90 1.7k
Ali Bahrami Rad United States 16 543 0.8× 914 1.8× 179 0.4× 482 2.4× 196 1.0× 52 1.4k
Fernando Andreotti United Kingdom 16 1.2k 1.8× 733 1.5× 465 1.1× 166 0.8× 412 2.1× 27 1.9k
Ramesh Kumar Sunkaria India 18 350 0.5× 558 1.1× 100 0.2× 112 0.5× 261 1.3× 94 956
Shoushui Wei China 21 658 1.0× 1.1k 2.2× 109 0.3× 277 1.4× 495 2.5× 87 1.6k
Radana Kahánková Czechia 19 320 0.5× 686 1.4× 318 0.8× 253 1.2× 422 2.2× 83 1.2k
Lina Zhao China 17 645 1.0× 1.1k 2.2× 87 0.2× 183 0.9× 424 2.2× 62 1.4k
Christian Jutten France 8 666 1.0× 387 0.8× 348 0.9× 68 0.3× 259 1.3× 10 1.1k

Countries citing papers authored by Shivnarayan Patidar

Since Specialization
Citations

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

Fields of papers citing papers by Shivnarayan Patidar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shivnarayan Patidar

This figure shows the co-authorship network connecting the top 25 collaborators of Shivnarayan Patidar. A scholar is included among the top collaborators of Shivnarayan Patidar 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 Shivnarayan Patidar. Shivnarayan Patidar 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.
2.
Sharma, Ashish, et al.. (2023). A scalogram tensor decomposition based ECG quality assessment. Journal of Electrocardiology. 81. 169–175. 1 indexed citations
3.
Patidar, Shivnarayan, et al.. (2022). Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification. Physiological Measurement. 43(12). 124002–124002. 2 indexed citations
4.
Patidar, Shivnarayan, et al.. (2021). Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis. Computers in Biology and Medicine. 134. 104430–104430. 19 indexed citations
5.
Patidar, Shivnarayan, et al.. (2021). A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data. Journal of Applied Biomedicine. 41(3). 1013–1024. 13 indexed citations
6.
Patidar, Shivnarayan, et al.. (2020). Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform. Journal of Applied Biomedicine. 41(1). 111–126. 23 indexed citations
7.
Sharma, Ashish, et al.. (2020). Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM. Computers in Biology and Medicine. 120. 103753–103753. 35 indexed citations
8.
Patidar, Shivnarayan. (2019). Diagnosis of Sepsis Using Ratio Based Features. Computing in Cardiology Conference. 1–4. 2 indexed citations
9.
Patidar, Shivnarayan. (2019). Diagnosis of Sepsis Using Ratio Based Features. Computing in cardiology. 45. 5 indexed citations
10.
Sharma, Ashish, Shivnarayan Patidar, Abhay Upadhyay, & U. Rajendra Acharya. (2019). Accurate tunable-Q wavelet transform based method for QRS complex detection. Computers & Electrical Engineering. 75. 101–111. 70 indexed citations
11.
12.
Patidar, Shivnarayan & Ram Bilas Pachori. (2016). Tunable-Q wavelet transform based optimal compression of cardiac sound signals. 2193–2197. 11 indexed citations
13.
Patidar, Shivnarayan, Ram Bilas Pachori, Abhay Upadhyay, & U. Rajendra Acharya. (2016). An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Applied Soft Computing. 50. 71–78. 97 indexed citations
14.
Kumar, Y. B. Nithin, et al.. (2016). Design and Implementation of Tunable Bandpass Filter for Biomedical Applications. 43–46. 4 indexed citations
15.
Patidar, Shivnarayan, Ram Bilas Pachori, & U. Rajendra Acharya. (2015). Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowledge-Based Systems. 82. 1–10. 145 indexed citations
16.
Patidar, Shivnarayan, et al.. (2014). Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals. Expert Systems with Applications. 42(7). 3315–3326. 76 indexed citations
17.
Patidar, Shivnarayan, et al.. (2014). Detection of septal defects from cardiac sound signals using tunable-Q wavelet transform. 15. 580–585. 3 indexed citations
18.
Patidar, Shivnarayan & Ram Bilas Pachori. (2014). Classification of cardiac sound signals using constrained tunable-Q wavelet transform. Expert Systems with Applications. 41(16). 7161–7170. 84 indexed citations
19.
Pachori, Ram Bilas & Shivnarayan Patidar. (2013). Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer Methods and Programs in Biomedicine. 113(2). 494–502. 217 indexed citations
20.
Patidar, Shivnarayan & Ram Bilas Pachori. (2013). Constrained Tunable-Q Wavelet Transform based Analysis of Cardiac Sound Signals. AASRI Procedia. 4. 57–63. 9 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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