Pedro Gomis

660 total citations
58 papers, 452 citations indexed

About

Pedro Gomis is a scholar working on Cardiology and Cardiovascular Medicine, Biomedical Engineering and Cognitive Neuroscience. According to data from OpenAlex, Pedro Gomis has authored 58 papers receiving a total of 452 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Cardiology and Cardiovascular Medicine, 8 papers in Biomedical Engineering and 5 papers in Cognitive Neuroscience. Recurrent topics in Pedro Gomis's work include ECG Monitoring and Analysis (37 papers), Cardiac electrophysiology and arrhythmias (36 papers) and Heart Rate Variability and Autonomic Control (23 papers). Pedro Gomis is often cited by papers focused on ECG Monitoring and Analysis (37 papers), Cardiac electrophysiology and arrhythmias (36 papers) and Heart Rate Variability and Autonomic Control (23 papers). Pedro Gomis collaborates with scholars based in Spain, United States and Venezuela. Pedro Gomis's co-authors include P. Caminal, Edward J. Berbari, Galen S. Wagner, Eduard Guasch, Alexandre Perera-Lluna, Lluı́s Mont, P. Lander, Douglas L. Jones, Paul Lander and Yannick Berthoumieu and has published in prestigious journals such as Circulation, PLoS ONE and Scientific Reports.

In The Last Decade

Pedro Gomis

51 papers receiving 435 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pedro Gomis Spain 11 383 107 61 38 38 58 452
Jana Krohová Slovakia 13 338 0.9× 194 1.8× 97 1.6× 21 0.6× 19 0.5× 33 411
Silvia Mezzetti Italy 8 399 1.0× 148 1.4× 75 1.2× 18 0.5× 13 0.3× 11 447
Martin Bachler Austria 10 233 0.6× 125 1.2× 67 1.1× 20 0.5× 19 0.5× 30 323
Rebeca Goya–Esteban Spain 12 203 0.5× 62 0.6× 75 1.2× 29 0.8× 13 0.3× 55 372
Ravi Komatireddy United States 6 321 0.8× 120 1.1× 40 0.7× 23 0.6× 22 0.6× 6 491
Hyung-Ro Yoon South Korea 9 273 0.7× 201 1.9× 70 1.1× 32 0.8× 18 0.5× 22 421
Laurence Keselbrener Israel 10 250 0.7× 165 1.5× 60 1.0× 18 0.5× 40 1.1× 17 358
Andrea Seeck Germany 10 294 0.8× 78 0.7× 22 0.4× 13 0.3× 39 1.0× 19 346
Changchun Liu China 10 313 0.8× 145 1.4× 157 2.6× 23 0.6× 18 0.5× 22 478

Countries citing papers authored by Pedro Gomis

Since Specialization
Citations

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

Fields of papers citing papers by Pedro Gomis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pedro Gomis

This figure shows the co-authorship network connecting the top 25 collaborators of Pedro Gomis. A scholar is included among the top collaborators of Pedro Gomis 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 Pedro Gomis. Pedro Gomis 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.
Gomis, Pedro, et al.. (2022). Weighted Time Warping Improves T-Wave Morphology Markers Clinical Significance. IEEE Transactions on Biomedical Engineering. 69(9). 2787–2796. 2 indexed citations
2.
Gomis, Pedro, José Esteban Ruiz, Alba Martín-Yebra, et al.. (2021). Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study. Sensors. 21(8). 2710–2710. 2 indexed citations
3.
Gomis, Pedro, José Esteban Ruiz, Alba Martín-Yebra, et al.. (2021). ECG-based monitoring of blood potassium concentration: Periodic versus principal component as lead transformation for biomarker robustness. Biomedical Signal Processing and Control. 68. 102719–102719. 9 indexed citations
4.
Gomis, Pedro, José Esteban Ruiz, Alba Martín-Yebra, et al.. (2021). Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics. Scientific Reports. 11(1). 3883–3883. 9 indexed citations
5.
Gomis, Pedro, José Esteban Ruiz, Alba Martín-Yebra, et al.. (2020). Potassium Monitoring from Multilead T-wave Morphology Changes during Hemodyalisis: Periodic versus Principal Component Analysis. Computing in cardiology. 2 indexed citations
6.
Gomis, Pedro, José Esteban Ruiz, Alba Martín-Yebra, et al.. (2019). T-Wave Morphology Changes as Surrogate for Blood Potassium Concentration in Hemodialysis Patients. Computing in cardiology. 4 indexed citations
7.
Ramírez, Julia, Pablo Laguna, Pedro Gomis, et al.. (2019). T-Wave Morphology Changes as Surrogate for Blood Potassium Concentration in Hemodialysis Patients. Zaguan (University of Zaragoza Repository). 1–4. 5 indexed citations
8.
RAVELO, A. G., et al.. (2019). Application of the Entropy of Approximation for the nonlinear characterization in patients with Chagas Disease. Acceda (Universidad de Las Palmas de Gran Canaria). 2019. 1–4. 1 indexed citations
9.
Romero, Daniel, Virginie Le Rolle, Nathalie Béhar, et al.. (2018). Multivariate classification of Brugada syndrome patients based on autonomic response to exercise testing. PLoS ONE. 13(5). e0197367–e0197367. 14 indexed citations
10.
Caminal, P., et al.. (2018). Validity of the Polar V800 monitor for measuring heart rate variability in mountain running route conditions. European Journal of Applied Physiology. 118(3). 669–677. 70 indexed citations
11.
Rolle, Virginie Le, Daniel Romero, Nathalie Béhar, et al.. (2018). Model-based analysis of the autonomic response to head-up tilt testing in Brugada syndrome. Computers in Biology and Medicine. 103. 82–92. 9 indexed citations
12.
Rolle, Virginie Le, Daniel Romero, Nathalie Béhar, et al.. (2018). Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night. Physiological Measurement. 39(6). 65002–65002. 3 indexed citations
13.
Rolle, Virginie Le, et al.. (2017). Time-frequency Analysis of the Autonomic Response to Head-up Tilt Testing in Brugada Syndrome. Computing in cardiology. 2 indexed citations
14.
Magrans, Rudys, Pedro Gomis, & P. Caminal. (2016). Myocardial ischemia event detection based on support vector machine model using QRS and ST segment features. Computing in Cardiology Conference. 2 indexed citations
15.
Gomis, Pedro & P. Caminal. (2014). Evaluation of very low amplitude intra-QRS potentials during the initial minutes of acute transmural myocardial ischemia. Journal of Electrocardiology. 47(4). 512–519. 5 indexed citations
16.
Vallverdú, Montserrat, Pedro Gomis, Alberto Porta, et al.. (2012). Symbolic dynamics of QT interval series in ischemic cardiomyopathy. QRU Quaderns de Recerca en Urbanisme. 617–620. 1 indexed citations
17.
Gomis, Pedro, P. Caminal, Montserrat Vallverdú, et al.. (2011). Assessment of autonomic control of the heart during transient myocardial ischemia. Journal of Electrocardiology. 45(1). 82–89. 8 indexed citations
18.
Dumont, Julie, et al.. (2009). Detection of myocardial ischemia with hidden Semi-Markovian models. HAL (Le Centre pour la Communication Scientifique Directe). 121–124. 3 indexed citations
19.
Berthoumieu, Yannick, et al.. (2007). ECG Beat Detection Using a Geometrical Matching Approach. IEEE Transactions on Biomedical Engineering. 54(4). 641–650. 41 indexed citations
20.
Mora, F., Pedro Gomis, & G. Passariello. (1999). Señales electrocardiográficas de alta resolución en Chagas: el proyecto search. 50(3). 187–194. 3 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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