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Felipe Meneguitti Dias

📧 f.meneguittidias@gmail.com
🔗 LinkedIn: felipe-meneguitti-dias-312570b3
🎓 Google Scholar: QYW9cngAAAAJ

Data Scientist and Researcher specialized in signal processing, computer vision, and machine learning.

Experience

Heart Institute (InCor-HCFMUSP) - Brazil
Research and Development (R&D) Engineer (2020 - Present)

  • Developed and refined machine learning/deep learning models for automated electrocardiogram classification \
  • Deployment of models in hospital facilities for improved diagnostic processes.

Education

University of Sao Paulo - Brazil
Ph.D. in Electrical Engineering (2021 - Present)
Thesis: Application of Deep Learning to Biomedical Signals
Advisor: Marco Antonio Gutierrez

Federal University of Juiz de Fora - Brazil
M.S. in Electrical Engineering (2018 - 2020)
Thesis: Methodology for Monitoring Large Structures Using Visual Descriptors

Illinois Institute of Technology - The United States
B.S. in Electrical Engineering (2015 - 2016)

Federal University of Juiz de Fora - Brazil
B.S. in Electronic Engineering (2012 - 2017)
Thesis: Integration of Infrared and Visual Imaging for Equipment Inspection

Awards

  • Science Without Borders Scholarship, 2015
  • Bronze Medal in Brazilian Physics Olympiad, Brazilian Society of Physics, 2011

Selected Publications

  • Artificial Intelligence-Driven Screening System for Rapid Image-Based Classification of 12-Lead ECG Exams
    IEEE Access, 2023 | Journal article
    DOI: 10.1109/ACCESS.2023.3328538
    Felipe Meneguitti Dias; Estela Ribeiro; Ramon Alfredo Moreno; Adèle Helena Ribeiro; Nelson Samesima; Carlos Alberto Pastore; Jose Eduardo Krieger; Marco Antonio Gutierrez

  • Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment
    IEEE Access, 2023 | Journal article
    DOI: 10.1109/ACCESS.2023.3284458
    Thiago Bulhões Da Silva Costa; Felipe Meneguitti Dias; Diego Armando Cardona Cardenas; Marcelo Arruda Fiuza De Toledo; Daniel Mário De Lima; Jose Eduardo Krieger; Marco Antonio Gutierrez

  • A Machine Learning Approach to Predict Arterial Blood Pressure from Photoplethysmography Signal
    Computing in Cardiology, 2022 | Conference paper
    DOI: 10.22489/CinC.2022.238
    Dias, F.M.; Costa, T.B.S.; Cardenas, D.A.C.; Toledo, M.A.F.; Krieger, J.E.; Gutierrez, M.A.

  • Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm
    Computer Methods and Programs in Biomedicine, 2021 | Journal article
    DOI: 10.1016/j.cmpb.2021.105948
    Felipe Meneguitti Dias; Henrique L.M. Monteiro; Thales Wulfert Cabral; Rayen Naji; Michael Kuehni; Eduardo José