Ghasem Azemi

1.1k total citations
54 papers, 749 citations indexed

About

Ghasem Azemi is a scholar working on Cognitive Neuroscience, Signal Processing and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Ghasem Azemi has authored 54 papers receiving a total of 749 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Cognitive Neuroscience, 21 papers in Signal Processing and 13 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Ghasem Azemi's work include EEG and Brain-Computer Interfaces (24 papers), Blind Source Separation Techniques (17 papers) and Neural dynamics and brain function (13 papers). Ghasem Azemi is often cited by papers focused on EEG and Brain-Computer Interfaces (24 papers), Blind Source Separation Techniques (17 papers) and Neural dynamics and brain function (13 papers). Ghasem Azemi collaborates with scholars based in Australia, Iran and Qatar. Ghasem Azemi's co-authors include B. Boashash, John M. O’Toole, Paul B. Colditz, Amir Omidvarnia, Nabeel Ali Khan, Larbi Boubchir, Md. Abdul Awal, Bouchra Senadji, Sampsa Vanhatalo and Melissa Lai and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Biomedical Engineering and Pattern Recognition.

In The Last Decade

Ghasem Azemi

53 papers receiving 738 citations

Peers

Ghasem Azemi

CCM

PPCH

Bertrand Rivet France

Hongxing Liu China

John M. O’Toole Ireland

Christian Jutten France

Mostefa Mesbah Australia

Radana Kahánková Czechia

Edmond Zahedi Malaysia

Ram Mani United States

Mehdi Chehel Amirani Iran

Ruhi Mahajan United States

Bertrand Rivet France

Ghasem Azemi

783 ×2.2

499 ×2.1

208 ×1.6

CCM

42 ×0.3

PPCH

140 ×1.2

Citations per year, relative to Ghasem Azemi Ghasem Azemi (= 1×) peers Bertrand Rivet

Countries citing papers authored by Ghasem Azemi

Since Specialization

Citations

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

Fields of papers citing papers by Ghasem Azemi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ghasem Azemi

This figure shows the co-authorship network connecting the top 25 collaborators of Ghasem Azemi. A scholar is included among the top collaborators of Ghasem Azemi 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 Ghasem Azemi. Ghasem Azemi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Azemi, Ghasem & Antonio Di Ieva. (2025). Radiomic Fingerprinting of the Peritumoral Edema in Brain Tumors. Cancers. 17(3). 478–478. 4 indexed citations

Molina, Eric Suero, Ghasem Azemi, Carlo Russo, Sidong Liu, & Antonio Di Ieva. (2024). Artificial Intelligence in Brain Tumors. Advances in experimental medicine and biology. 1462. 201–220. 1 indexed citations

Molina, Eric Suero, Ghasem Azemi, Zeynep Canan Özdemir, et al.. (2024). Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics. Journal of Neuro-Oncology. 171(3). 589–598. 1 indexed citations

Molina, Eric Suero, et al.. (2023). Unraveling the blue shift in porphyrin fluorescence in glioma: The 620 nm peak and its potential significance in tumor biology. Frontiers in Neuroscience. 17. 1261679–1261679. 12 indexed citations

Azemi, Ghasem, et al.. (2021). Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD. Computers in Biology and Medicine. 134. 104515–104515. 27 indexed citations

Zarjam, Pega, et al.. (2021). Fetal ECG Extraction from Sparse Representation of Multichannel Abdominal Recordings. Circuits Systems and Signal Processing. 41(4). 2027–2044. 8 indexed citations

Awal, Md. Abdul, Melissa Lai, Ghasem Azemi, B. Boashash, & Paul B. Colditz. (2015). EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review. Clinical Neurophysiology. 127(1). 285–296. 71 indexed citations

Awal, Md. Abdul, et al.. (2015). Detection of neonatal EEG burst-suppression using time-frequency matching pursuit. 6. 1 indexed citations

Boashash, B. & Ghasem Azemi. (2014). A review of time–frequency matched filter design with application to seizure detection in multichannel newborn EEG. Digital Signal Processing. 28. 28–38. 29 indexed citations

10.

Omidvarnia, Amir, Ghasem Azemi, Paul B. Colditz, & B. Boashash. (2013). A time–frequency based approach for generalized phase synchrony assessment in nonstationary multivariate signals. Digital Signal Processing. 23(3). 780–790. 20 indexed citations

11.

Boashash, B., et al.. (2013). Automated detection of perinatal hypoxia using time–frequency-based heart rate variability features. Medical & Biological Engineering & Computing. 52(2). 183–191. 9 indexed citations

12.

Omidvarnia, Amir, Ghasem Azemi, B. Boashash, et al.. (2013). Measuring Time-Varying Information Flow in Scalp EEG Signals: Orthogonalized Partial Directed Coherence. IEEE Transactions on Biomedical Engineering. 61(3). 680–693. 70 indexed citations

13.

Boashash, B., et al.. (2013). Detection of perinatal hypoxia using time-frequency analysis of heart rate variability signals. Qatar University QSpace (Qatar University). 38. 939–943. 3 indexed citations

14.

Azemi, Ghasem, et al.. (2013). Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels. Biomedical Signal Processing and Control. 10. 153–165. 15 indexed citations

15.

Azemi, Ghasem, et al.. (2011). Automatic epilepsy detection using the instantaneous frequency and sub-band energies of the EEG signals. Queensland's institutional digital repository (The University of Queensland). 1–5. 2 indexed citations

16.

Boashash, B., Larbi Boubchir, & Ghasem Azemi. (2011). Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities. Qatar University QSpace (Qatar University). 80. 120–129. 17 indexed citations

17.

Azemi, Ghasem, et al.. (2008). Estimating the scattering distribution of the received signal in micro-cellular systems. European Signal Processing Conference. 1–4. 2 indexed citations

18.

Azemi, Ghasem, B. Boashash, Mostefa Mesbah, & Pega Zarjam. (2005). Newborns' EEG Seizure Detection Using T-F Distance Measures. Queensland's institutional digital repository (The University of Queensland). 23(1). 163–171. 1 indexed citations

19.

Azemi, Ghasem, Bouchra Senadji, & B. Boashash. (2002). Velocity estimation in cellular systems based on the time-frequency characteristics of the received signal. 2. 509–512. 6 indexed citations

20.

Azemi, Ghasem, Bouchra Senadji, & B. Boashash. (2002). Estimation of the velocity of mobile units in microcellular systems using the instantaneous frequency of the received signals. 2. 1025–1028. 1 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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