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Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several ti...

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Autores principales: Pinto, Jorge, González, Hernando, Arizmendi, Carlos, González, Hernán, Muñoz, Yecid, Giraldo, Beatriz F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002224/
https://www.ncbi.nlm.nih.gov/pubmed/36901440
http://dx.doi.org/10.3390/ijerph20054430
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author Pinto, Jorge
González, Hernando
Arizmendi, Carlos
González, Hernán
Muñoz, Yecid
Giraldo, Beatriz F.
author_facet Pinto, Jorge
González, Hernando
Arizmendi, Carlos
González, Hernán
Muñoz, Yecid
Giraldo, Beatriz F.
author_sort Pinto, Jorge
collection PubMed
description The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.
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spelling pubmed-100022242023-03-11 Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence Pinto, Jorge González, Hernando Arizmendi, Carlos González, Hernán Muñoz, Yecid Giraldo, Beatriz F. Int J Environ Res Public Health Article The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients. MDPI 2023-03-01 /pmc/articles/PMC10002224/ /pubmed/36901440 http://dx.doi.org/10.3390/ijerph20054430 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pinto, Jorge
González, Hernando
Arizmendi, Carlos
González, Hernán
Muñoz, Yecid
Giraldo, Beatriz F.
Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title_full Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title_fullStr Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title_full_unstemmed Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title_short Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence
title_sort analysis of the cardiorespiratory pattern of patients undergoing weaning using artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002224/
https://www.ncbi.nlm.nih.gov/pubmed/36901440
http://dx.doi.org/10.3390/ijerph20054430
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