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

Jorge Pinto, Hernando González (), Carlos Arizmendi, Hernán González, Yecid Muñoz and Beatriz F. Giraldo
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Jorge Pinto: Faculty of Engineering, Universidad Autónoma de Bucaramanga; Bucaramanga 680003, Colombia
Hernando González: Faculty of Engineering, Universidad Autónoma de Bucaramanga; Bucaramanga 680003, Colombia
Carlos Arizmendi: Faculty of Engineering, Universidad Autónoma de Bucaramanga; Bucaramanga 680003, Colombia
Hernán González: Faculty of Engineering, Universidad Autónoma de Bucaramanga; Bucaramanga 680003, Colombia
Yecid Muñoz: Faculty of Engineering, Universidad Autónoma de Bucaramanga; Bucaramanga 680003, Colombia
Beatriz F. Giraldo: Automatic Control Department (ESAII), The Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya (UPC), 08019 Barcelona, Spain

IJERPH, 2023, vol. 20, issue 5, 1-14

Abstract: 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.

Keywords: mechanical ventilation; weaning; wavelet transform; neural networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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