Everton is currently a Visiting Instructor at Stanford University, Division of Cardiovascular Medicine in the School of Medicine.
He has received his BSc degree in Electrical Engineering (2019) and MSc degree in Computer Science (2021) by the State University of Londrina (UEL), Brazil. From 2015 to 2016, he was an exchange student at Hanze University of Applied Sciences, the Netherlands, where he followed minors in Biomedical and Sensor System Engineering.
He has worked with several Research and Development projects, englobing Machine Learning and Instrumentation Engineering applied to many domains.
He has also worked as a Professor at the Pontifical Catholic University of Paraná in the Cyber-Physical Systems theme.
His current research interest is in Data Science applied to Biomedical Signals.

Academic Appointments

Honors & Awards

  • 32nd Science and Technology Award of Paraná - Undergraduate in Exact and Earth Sciences Category, General Secretariat for Science, Technology and Higher Education - Government of Paraná State (BR) (2019)
  • Master’s Scholarship, Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) (2019-2020)
  • Honorable Mention Award - Evaluating the Four-way Performance Trade-off for Stream Classification, 4th International Conference on Green, Pervasive and Cloud Computing (2019)
  • PIBIC Undergraduate Research Scholarship, Brazilian Council for Scientific and Technological Development (CNPq) (2017-2018)
  • Science without Borders Undergraduate Exchange Scholarship, Brazilian Council for Scientific and Technological Development (CNPq) (2015-2016)
  • PICME Undergraduate Research Scholarship, Brazilian Council for Scientific and Technological Development (CNPq) (2013-2015)
  • Silver Medal in the Brazilian Mathematics Olympiad for Public Schools, Brazilian Pure and Applied Math Association, Ministry of Education, Science and Technology et al. (2009)
  • Honors in the Brazilian Mathematics Olympiad for Public Schools, Brazilian Pure and Applied Math Association, Ministry of Education, Science and Technology et al. (2007, 2012)

Professional Education

  • Master, State University of Londrina, Computer Science (Computational Intelligence) (2021)
  • Bachelor, State University of Londrina, Electrical Engineering (2019)
  • Minor (Exchange), Hanze Institute of Technology, Sensor Systems Engineering (2016)
  • Minor (Exchange), Hanze University of Applied Sciences, Biomedical Engineering (2015)

All Publications

  • Right Ventricular Dysfunction Patterns Among Patients with COVID-19 in the Intensive Care Unit - a Retrospective Cohort Analysis. Annals of the American Thoracic Society Sanchez, P. A., O'Donnell, C. T., Francisco, N., Santana, E. J., Moore, A. R., Pacheco-Navarro, A., Roque, J., Lebold, K. M., Parmer, C. M., Pienkos, S. M., Celestin, B. E., Levitt, J. E., Collins, W. J., Lanspa, M. J., Ashley, E. A., Wilson, J. G., Haddad, F., Rogers, A. J. 2023


    Right ventricular (RV) dysfunction is common among patients hospitalized with COVID-19; however, its epidemiology may depend on the echocardiographic parameters used to define it.To evaluate the prevalence of abnormalities in three common echocardiographic parameters of RV function among COVID-19 patients admitted to the intensive care unit, as well as the effect of RV dilatation on differential parameter abnormality and the association of RV dysfunction with 60-day mortality.Retrospective cohort study of COVID-19 ICU patients between March 4th,2020 to March 4th, 2021, who received a transthoracic echocardiogram within 48 hours before to at most 7 days after ICU admission. RV dysfunction and dilatation respectively defined by guideline thresholds for tricuspid annular plane systolic excursion (TAPSE), RV fractional area change (RVFAC), RV free wall longitudinal strain (RVFWS), and RV basal dimension or RV end-diastolic area. Association of RV dysfunction with 60-day mortality assessed through logistic regression adjusting for age, prior history of congestive heart failure, invasive ventilation at time of TTE and APACHE II score.116 patients were included, of which 69% had RV dysfunction by > 1 parameter and 36.3% of these had RV dilatation. The three most common patterns of RV dysfunction included: Presence of 3 abnormalities, the combination of abnormal RVFWS and TAPSE, and isolated TAPSE abnormality. Patients with RV dilatation had worse RVFAC (24% vs 36%, p = 0.001), worse RVFWS (16.3% vs 19.1%, p = 0.005), higher RVSP (45mmHg vs 31mmHg, p = 0.001) but similar TAPSE (13mm vs 13mm, p = 0.30) compared to those with normal RV size. After multivariable adjustment, 60-day mortality was significantly associated with RV dysfunction (OR 2.91, 95% CI 1.01 - 9.44), as was the presence of at least 2 parameter abnormalities.ICU patients with COVID-19 had significant heterogeneity in RV function abnormalities present with different patterns associated with RV dilatation. RV dysfunction by any parameter was associated with increased mortality. Therefore, a multiparameter evaluation may be critical in recognizing RV dysfunction in COVID-19.

    View details for DOI 10.1513/AnnalsATS.202303-235OC

    View details for PubMedID 37478340

  • Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners SPEECH COMMUNICATION Barbon Junior, S., Guido, R., Aguiar, G., Santana, E., Proenca Junior, M., Patil, H. A. 2023; 152
  • Novel left ventricular mechanical index in pulmonary arterial hypertension. Pulmonary circulation Ichimura, K., Santana, E. J., Kuznetsova, T., Cauwenberghs, N., Sabovčik, F., Chun, L., Francisco, N. L., Kheyfets, V. O., Salerno, M., Zamanian, R. T., Spiekerkoetter, E., Haddad, F. 2023; 13 (2): e12216


    Ventricular interdependence plays an important role in pulmonary arterial hypertension (PAH). It can decrease left ventricular (LV) longitudinal strain (LVLS) and lead to a leftward displacement ("transverse shortening") of the interventricular septum (sTS). For this study, we hypothesized the ratio of LVLS/sTS would be a sensitive marker of systolic ventricular interactions in PAH. In a cross-sectional cohort of patients with PAH (n = 57) and matched controls (n = 57), we quantified LVLS and septal TS in the amplitude and time domain. We then characterized LV phenotypes using upset plots, ventricular interactions using network analysis, and longitudinal analysis in a representative cohort of 45 patients. We also measured LV metrics in mice subjected to pulmonary arterial banding (PAB) using a 7 T magnetic resonance imaging at baseline, Week 1, and Week 7 post-PAB (N = 9). Patients with PAH had significantly reduced absolute LVLS (15.4 ± 3.4 vs. 20.1 ± 2.3%, p < 0.0001), higher sTS (53.0 ± 12.2 vs. 28.0 ± 6.2%, p < 0.0001) and lower LVLS/sTS (0.30 ± 0.09 vs. 0.75 ± 0.16, p < 0.0001) compared to controls. Reduced LVLS/sTS was observed in 89.5% of patients, while diastolic dysfunction, impaired LVLS (<16%), and LV atrophy were observed in 73.7%, 52.6%, and 15.8%, respectively. In the longitudinal cohort, changes in LVLS/sTS were closely associated with changes in N-terminal pro B-type natriuretic peptide (r = 0.73, p < 0.0001) as well as survival. Mice subjected to PAB showed significant RV systolic dysfunction and decreased LVLS/sTS compared to sham animals. We conclude that in PAH, LVLV/sTS is a simple ratio that can reflect ventricular systolic interactions.

    View details for DOI 10.1002/pul2.12216

    View details for PubMedID 37063750

    View details for PubMedCentralID PMC10103585

  • Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial. Clinical nutrition ESPEN Haddad, F., Li, X., Perelman, D., Santana, E. J., Kuznetsova, T., Cauwenberghs, N., Busque, V., Contrepois, K., Snyder, M. P., Leonard, M. B., Gardner, C. 2023; 53: 43-52


    BACKGROUND & AIMS: Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention.DESIGN: Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340at 6 months and 323at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention.RESULTS: A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM0.66*FM0.066] was body size and sex independent (p=0.91 and p=0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r=-0.47 for female and r=-0.44 for male, both p<0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p<0.001) and between individuals who are overweight and obese (p<0.001) while no significant differences were observed when indexed to REE/[LBM0.66*FM0.066], p>0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p=0.614).CONCLUSION: Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

    View details for DOI 10.1016/j.clnesp.2022.11.015

    View details for PubMedID 36657929

  • Using meta-learning for multi-target regression INFORMATION SCIENCES Aguiar, G. J., Santana, E. J., de Carvalho, A. L., Barbon Junior, S. 2022; 584: 665-684
  • Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study INFORMATION Santana, E., Silva, R., Zarpelao, B., Barbon Junior, S. 2021; 12 (10)
  • Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS Santana, E., dos Santos, F., Mastelini, S., Melquiades, F., Barbon Jr, S. 2021; 209
  • Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT Lopes, J., Santana, E., Turrisi da Costa, V. G., Zarpelao, B., Barbon Junior, S. 2020; 17 (2): 1013-1025
  • DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking APPLIED SOFT COMPUTING Mastelini, S., Santana, E., Cerri, R., Barbon Jr, S. 2020; 91
  • Advantages of Multi-Target Modelling for Spectral Regression Spectroscopic Techniques & Artificial Intelligence for Food and Beverage Analysis Barbon Jr., S., Santana, E. J., Badaró, A. T., Borrás, N. A., Barbin, D. F. edited by Shukla, A. K. Springer Singapore. 2020: 95-121
  • Photovoltaic Generation Forecast: Model Training and Adversarial Attack Aspects Santana, E. J., Silva, R. P., Zarpelão, B. B., Barbon Jr., S. 2020: 634-649
  • Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY Mastelini, S., Turrisi da Costa, V., Santana, E., Nakano, F., Guido, R., Cerri, R., Barbon, S. 2019; 91 (2): 191-215
  • Towards Meta-Learning for Multi-Target Regression Problems Aguiar, G. J., Santana, E. J., Mastelini, S. M., Mantovani, R. G., Barbon Jr., S. 2019: 337-382
  • Evaluating the Four-Way Performance Trade-Off for Stream Classification Turrisi da Costa, V. G., Santana, E., Lopes, J. F., Barbon, S., Miani, R., Camargos, L., Zarpelao, B., Rosas, E., Pasquini, R. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 3-17
  • Predicting poultry meat characteristics using an enhanced multi-target regression method BIOSYSTEMS ENGINEERING Santana, E. J., Geronimo, B. C., Mastelini, S. M., Carvalho, R. H., Barbin, D. F., Ida, E. I., Barbon, S. 2018; 171: 193-204
  • Benchmarking multi-target regression methods Mastelini, S., Santana, E., Turrisi da Costa, V. G., Barbon, S., IEEE IEEE. 2018: 396-401
  • Stock Portfolio Prediction by Multi-Target Decision Support Provin Ribeiro da Silva, J., Santana, E., Mastelini, S., Barbon, S., ACM ASSOC COMPUTING MACHINERY. 2018: 262-269
  • DSTARS: A Multi-Target Deep Structure for Tracking Asynchronous Regressor Stack Mastelini, S., Santana, E., Cerri, R., Barbon, S., IEEE IEEE COMPUTER SOC. 2017: 19-24