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Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model

One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is...

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Autores principales: Benouar, Sara, Kedir-Talha, Malika, Seoane, Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280448/
https://www.ncbi.nlm.nih.gov/pubmed/37346485
http://dx.doi.org/10.3389/fphys.2023.1181745
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author Benouar, Sara
Kedir-Talha, Malika
Seoane, Fernando
author_facet Benouar, Sara
Kedir-Talha, Malika
Seoane, Fernando
author_sort Benouar, Sara
collection PubMed
description One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%–30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets.
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spelling pubmed-102804482023-06-21 Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model Benouar, Sara Kedir-Talha, Malika Seoane, Fernando Front Physiol Physiology One of the crucial steps in assessing hemodynamic parameters using impedance cardiography (ICG) is the detection of the characteristic points in the dZ/dt ICG complex, especially the X point. The most often estimated parameters from the ICG complex are stroke volume and cardiac output, for which is required the left ventricular pre-ejection time. Unfortunately, for beat-to-beat calculations, the accuracy of detection is affected by the variability of the ICG complex subtypes. Thus, in this work, we aim to create a predictive model that can predict the missing points and decrease the previous work percentages of missing points to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. Thus, a time-series non-linear autoregressive model with exogenous inputs (NARX) feedback neural network approach was implemented to forecast the missing ICG points according to the different existing subtypes. The NARX was trained on two different datasets with an open-loop mode to ensure that the network is fed with correct feedback inputs. Once the training is satisfactory, the loop can be closed for multi-step prediction tests and simulation. The results show that we can predict the missing characteristic points in all the complexes with a success rate ranging between 75% and 88% in the evaluated datasets. Previously, without the NARX predictive model, the successful detection rate was 21%–30% for the same datasets. Thus, this work indicates a promising method and an accuracy increase in the detection of X, Y, O, and Z points for both datasets. Frontiers Media S.A. 2023-06-06 /pmc/articles/PMC10280448/ /pubmed/37346485 http://dx.doi.org/10.3389/fphys.2023.1181745 Text en Copyright © 2023 Benouar, Kedir-Talha and Seoane. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Benouar, Sara
Kedir-Talha, Malika
Seoane, Fernando
Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title_full Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title_fullStr Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title_full_unstemmed Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title_short Time-series NARX feedback neural network for forecasting impedance cardiography ICG missing points: a predictive model
title_sort time-series narx feedback neural network for forecasting impedance cardiography icg missing points: a predictive model
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280448/
https://www.ncbi.nlm.nih.gov/pubmed/37346485
http://dx.doi.org/10.3389/fphys.2023.1181745
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