Jorge Mateo

957 total citations
60 papers, 611 citations indexed

About

Jorge Mateo is a scholar working on Cognitive Neuroscience, Cardiology and Cardiovascular Medicine and Signal Processing. According to data from OpenAlex, Jorge Mateo has authored 60 papers receiving a total of 611 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cognitive Neuroscience, 14 papers in Cardiology and Cardiovascular Medicine and 14 papers in Signal Processing. Recurrent topics in Jorge Mateo's work include EEG and Brain-Computer Interfaces (17 papers), Blind Source Separation Techniques (12 papers) and ECG Monitoring and Analysis (12 papers). Jorge Mateo is often cited by papers focused on EEG and Brain-Computer Interfaces (17 papers), Blind Source Separation Techniques (12 papers) and ECG Monitoring and Analysis (12 papers). Jorge Mateo collaborates with scholars based in Spain, Germany and Russia. Jorge Mateo's co-authors include Eva María Sánchez‐Morla, Alejandro L. Borja, Alex Torres, J.J. Rieta, José Luis Santos, José Luis Santos, José Santos, Alexandra Bagney, Roberto Rodríguez–Jiménez and Miguel Ángel Jiménez‐Arriero and has published in prestigious journals such as Brain Research, International Journal of Molecular Sciences and British Journal of Haematology.

In The Last Decade

Jorge Mateo

58 papers receiving 607 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jorge Mateo Spain 13 195 153 104 66 64 60 611
Sandy Rihana Lebanon 12 317 1.6× 76 0.5× 109 1.0× 88 1.3× 117 1.8× 35 598
Shyam Vasudeva Rao Netherlands 15 377 1.9× 127 0.8× 137 1.3× 37 0.6× 146 2.3× 33 831
Wenliang Che China 11 376 1.9× 139 0.9× 44 0.4× 42 0.6× 114 1.8× 14 584
Tomás Teijeiro Switzerland 11 190 1.0× 218 1.4× 28 0.3× 92 1.4× 113 1.8× 36 612
Morteza Zabihi Finland 11 314 1.6× 316 2.1× 43 0.4× 54 0.8× 205 3.2× 21 663
Filip Plešinger Czechia 19 340 1.7× 634 4.1× 78 0.8× 108 1.6× 44 0.7× 81 928
Joseph Suresh Paul India 14 593 3.0× 170 1.1× 59 0.6× 118 1.8× 132 2.1× 63 1.0k
Maziyar Baran Pouyan United States 15 243 1.2× 228 1.5× 36 0.3× 335 5.1× 110 1.7× 36 868
Kais Gadhoumi United States 10 307 1.6× 283 1.8× 92 0.9× 233 3.5× 105 1.6× 16 660
Ram Mani United States 14 526 2.7× 108 0.7× 592 5.7× 77 1.2× 72 1.1× 28 1.4k

Countries citing papers authored by Jorge Mateo

Since Specialization
Citations

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

Fields of papers citing papers by Jorge Mateo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jorge Mateo

This figure shows the co-authorship network connecting the top 25 collaborators of Jorge Mateo. A scholar is included among the top collaborators of Jorge Mateo 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 Jorge Mateo. Jorge Mateo 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.
Mateo, Jorge, et al.. (2025). Improving prediction of fragility fractures in postmenopausal women using random forest. Computers in Biology and Medicine. 196(Pt A). 110666–110666.
2.
Mateo, Jorge, et al.. (2025). Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification. Life. 15(3). 394–394. 1 indexed citations
3.
Torralba, Miguel, et al.. (2024). Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques. Metabolites. 14(6). 305–305. 1 indexed citations
4.
5.
Torralba, Miguel, et al.. (2024). Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma. International Journal of Molecular Sciences. 25(4). 1996–1996. 5 indexed citations
6.
Mateo, Jorge, et al.. (2024). Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares. Bioengineering. 11(1). 90–90. 8 indexed citations
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Cascón, J., et al.. (2023). Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms. Schizophrenia Research. 261. 36–46. 8 indexed citations
10.
Martínez, Raquel, et al.. (2023). A Machine Learning-Based Method for Detecting Liver Fibrosis. Diagnostics. 13(18). 2952–2952. 4 indexed citations
11.
Torres, Alfonso Parreño, et al.. (2023). Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic. IRBM. 44(5). 100795–100795. 6 indexed citations
12.
Cascón, J., et al.. (2023). Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus. Journal of Investigative Medicine. 71(7). 742–752. 3 indexed citations
13.
Redondo, Miguel Á., et al.. (2023). Method for Classifying Schizophrenia Patients Based on Machine Learning. Journal of Clinical Medicine. 12(13). 4375–4375. 10 indexed citations
14.
Torres, Alex, et al.. (2022). Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model. Internal and Emergency Medicine. 17(7). 1929–1939. 5 indexed citations
15.
Torres, Alex, et al.. (2022). Prediction of patients with idiopathic generalized epilepsy from healthy controls using machine learning from scalp EEG recordings. Brain Research. 1798. 148131–148131. 18 indexed citations
16.
Nieto, J. Aizpurua, Jorge Mateo, Behnood Bikdeli, et al.. (2021). Machine Learning to Predict Outcomes in Patients with Acute Pulmonary Embolism Who Prematurely Discontinued Anticoagulant Therapy. Thrombosis and Haemostasis. 122(4). 570–577. 21 indexed citations
17.
Mateo, Jorge, et al.. (2019). Early detection of neonatal hearing loss by otoacoustic emissions and auditory brainstem response over 10 years of experience. International Journal of Pediatric Otorhinolaryngology. 127. 109647–109647. 12 indexed citations
18.
Belenguer, Ángel, et al.. (2018). Slotted ESIW Antenna With High Efficiency for a MIMO Radar Sensor. Radio Science. 53(5). 605–610. 7 indexed citations
19.
Mateo, Jorge & J.J. Rieta. (2012). Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Computers in Biology and Medicine. 43(2). 154–163. 16 indexed citations
20.
Mateo, Jorge & J.J. Rieta. (2012). Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings. Journal of Medical Engineering & Technology. 36(2). 90–101. 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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