Biosignals learning and synthesis using deep neural networks

David Belo, João Rodrigues, João R. Vaz, Pedro Pezarat-Correia, Hugo Gamboa

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
77 Downloads (Pure)


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. Method: The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. Results and conclusions: The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Original languageEnglish
Article number115
JournalBioMedical Engineering Online
Issue number1
Publication statusPublished - 25 Sep 2017


  • Biosignals
  • DNN
  • ECG
  • EMG
  • GRU
  • Neural networks
  • RESP
  • Synthesis

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