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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10002224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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