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Biosignals learning and synthesis using deep neural networks

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...

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Autores principales: Belo, David, Rodrigues, João, Vaz, João R., Pezarat-Correia, Pedro, Gamboa, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613402/
https://www.ncbi.nlm.nih.gov/pubmed/28946919
http://dx.doi.org/10.1186/s12938-017-0405-0
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author Belo, David
Rodrigues, João
Vaz, João R.
Pezarat-Correia, Pedro
Gamboa, Hugo
author_facet Belo, David
Rodrigues, João
Vaz, João R.
Pezarat-Correia, Pedro
Gamboa, Hugo
author_sort Belo, David
collection PubMed
description 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.
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spelling pubmed-56134022017-10-11 Biosignals learning and synthesis using deep neural networks Belo, David Rodrigues, João Vaz, João R. Pezarat-Correia, Pedro Gamboa, Hugo Biomed Eng Online Research 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. BioMed Central 2017-09-25 /pmc/articles/PMC5613402/ /pubmed/28946919 http://dx.doi.org/10.1186/s12938-017-0405-0 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Belo, David
Rodrigues, João
Vaz, João R.
Pezarat-Correia, Pedro
Gamboa, Hugo
Biosignals learning and synthesis using deep neural networks
title Biosignals learning and synthesis using deep neural networks
title_full Biosignals learning and synthesis using deep neural networks
title_fullStr Biosignals learning and synthesis using deep neural networks
title_full_unstemmed Biosignals learning and synthesis using deep neural networks
title_short Biosignals learning and synthesis using deep neural networks
title_sort biosignals learning and synthesis using deep neural networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613402/
https://www.ncbi.nlm.nih.gov/pubmed/28946919
http://dx.doi.org/10.1186/s12938-017-0405-0
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