George Manis

1.2k total citations
44 papers, 759 citations indexed

About

George Manis is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, George Manis has authored 44 papers receiving a total of 759 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Cardiology and Cardiovascular Medicine, 13 papers in Artificial Intelligence and 10 papers in Biomedical Engineering. Recurrent topics in George Manis's work include Heart Rate Variability and Autonomic Control (16 papers), ECG Monitoring and Analysis (10 papers) and Non-Invasive Vital Sign Monitoring (8 papers). George Manis is often cited by papers focused on Heart Rate Variability and Autonomic Control (16 papers), ECG Monitoring and Analysis (10 papers) and Non-Invasive Vital Sign Monitoring (8 papers). George Manis collaborates with scholars based in Greece, Italy and Belgium. George Manis's co-authors include Christophoros Nikou, Roberto Sassi, Md Aktaruzzaman, Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Stavros D. Nikolopoulos, Maria I. Argyropoulou, G. Papakonstantinou, Dimitrios I. Fotiadis and Πέτρος Αρσένος and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Software Engineering and Signal Processing.

In The Last Decade

George Manis

43 papers receiving 720 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
George Manis Greece 15 332 204 189 142 127 44 759
Ahmet Mert Türkiye 10 149 0.4× 341 1.7× 141 0.7× 91 0.6× 112 0.9× 28 660
Fatma Lati̇foğlu Türkiye 16 163 0.5× 245 1.2× 132 0.7× 146 1.0× 86 0.7× 81 767
David Cuesta–Frau Spain 19 401 1.2× 351 1.7× 136 0.7× 254 1.8× 208 1.6× 72 1.1k
D.L. Hudson United States 10 170 0.5× 83 0.4× 179 0.9× 80 0.6× 61 0.5× 60 525
Mosabber Uddin Ahmed Bangladesh 14 202 0.6× 235 1.2× 112 0.6× 158 1.1× 116 0.9× 32 855
Pushpendra Singh India 20 344 1.0× 518 2.5× 182 1.0× 228 1.6× 270 2.1× 49 1.2k
A. Petrosian United States 7 155 0.5× 356 1.7× 164 0.9× 100 0.7× 216 1.7× 19 721
Ramesh Kumar Sunkaria India 18 558 1.7× 350 1.7× 86 0.5× 261 1.8× 100 0.8× 94 956
Hongxing Liu China 18 252 0.8× 400 2.0× 70 0.4× 143 1.0× 137 1.1× 94 958
Lina Zhao China 17 1.1k 3.3× 645 3.2× 117 0.6× 424 3.0× 87 0.7× 62 1.4k

Countries citing papers authored by George Manis

Since Specialization
Citations

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

Fields of papers citing papers by George Manis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of George Manis

This figure shows the co-authorship network connecting the top 25 collaborators of George Manis. A scholar is included among the top collaborators of George Manis 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 George Manis. George Manis 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.
Manis, George & Roberto Sassi. (2021). A Python Library with Fast Algorithms for Popular Entropy Definitions. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 1–4. 2 indexed citations
2.
Rivolta, Massimo W., et al.. (2020). Analytical Formulation of Bubble Entropy for Autoregressive Processes. 1–2. 2 indexed citations
3.
Manis, George, et al.. (2020). Estimation of HRV Based on Low Frequency Data Transmission. Studies in health technology and informatics. 273. 255–257. 1 indexed citations
4.
Manis, George, Md Aktaruzzaman, & Roberto Sassi. (2018). Low Computational Cost for Sample Entropy. Entropy. 20(1). 61–61. 51 indexed citations
5.
Manis, George & Roberto Sassi. (2017). Tolerance to Spikes: a Comparison of Sample and Bubble Entropy. Computing in cardiology. 44. 1 indexed citations
6.
Manis, George, Md Aktaruzzaman, & Roberto Sassi. (2017). Bubble Entropy: An Entropy Almost Free of Parameters. IEEE Transactions on Biomedical Engineering. 64(11). 2711–2718. 104 indexed citations
7.
Αρσένος, Πέτρος & George Manis. (2014). Deceleration Capacity of heart rate: Two new methods of computation. Biomedical Signal Processing and Control. 14. 158–163. 7 indexed citations
8.
Αρσένος, Πέτρος, Konstantinos Gatzoulis, Polychronis Dilaveris, et al.. (2014). Arrhythmic sudden cardiac death: substrate, mechanisms and current risk stratification strategies for the post-myocardial infarction patient.. PubMed. 54(4). 301–15. 21 indexed citations
9.
Αρσένος, Πέτρος, George Manis, Stavros D. Nikolopoulos, et al.. (2013). Deceleration capacity alterations before Non-Sustained Ventricular Tachycardia episodes in post myocardial infarction patients. Computing in Cardiology Conference. 145–148. 1 indexed citations
10.
Manis, George, Stavros D. Nikolopoulos, Πέτρος Αρσένος, et al.. (2013). Risk stratification for Arrhythmic Sudden Cardiac Death in heart failure patients using machine learning techniques. Computing in Cardiology Conference. 141–144. 16 indexed citations
11.
Tripoliti, Evanthia E., Dimitrios I. Fotiadis, & George Manis. (2011). Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm. IEEE Transactions on Information Technology in Biomedicine. 16(4). 615–622. 35 indexed citations
12.
Tripoliti, Evanthia E., Dimitrios I. Fotiadis, & George Manis. (2010). Dynamic construction of Random Forests: Evaluation using biomedical engineering problems. 1–4. 15 indexed citations
13.
Tripoliti, Evanthia E., Dimitrios I. Fotiadis, Maria I. Argyropoulou, & George Manis. (2009). A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data. Journal of Biomedical Informatics. 43(2). 307–320. 57 indexed citations
14.
Manis, George, et al.. (2009). Heartbeat Time Series Classification With Support Vector Machines. IEEE Transactions on Information Technology in Biomedicine. 13(4). 512–518. 175 indexed citations
15.
Manis, George. (2009). Comparison of the most common HRV computation algorithms from the systems designer point of view. Journal of Medical Engineering & Technology. 33(2). 110–118. 2 indexed citations
16.
Manis, George. (2008). Fast computation of approximate entropy. Computer Methods and Programs in Biomedicine. 91(1). 48–54. 47 indexed citations
17.
Nikou, Christophoros, et al.. (2006). Robustness of Support Vector Machine-based Classification of Heart Rate Signals. PubMed. 2006. 2159–2162. 12 indexed citations
18.
Manis, George, et al.. (2006). Assessment of the classification capability of prediction and approximation methods for HRV analysis. Computers in Biology and Medicine. 37(5). 642–654. 16 indexed citations
19.
Manis, George, et al.. (2004). R-peak detection with alternative Haar wavelet filter. DSpace - NTUA (National Technical University of Athens). 3. 219–222. 4 indexed citations
20.

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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