Biosignals learning and synthesis using deep neural networks.

Journal: Biomedical engineering online
Published Date:

Abstract

BACKGROUND: Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.

Authors

  • David Belo
    LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal. dj.belo@fct.unl.pt.
  • João Rodrigues
    LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal.
  • João R Vaz
    Laboratory of Motor Behaviour, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, 1499-002, Cruz Quebrada - Dafundo, Portugal.
  • Pedro Pezarat-Correia
    Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
  • Hugo Gamboa
    LIBPhys-UNL, Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal.