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

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2 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Número de artículo4430
Páginas (desde-hasta)1-14
Número de páginas14
PublicaciónInternational Journal of Environmental Research and Public Health
Volumen20
N.º5
DOI
EstadoPublicada - 1 mar. 2023

Palabras clave

  • neural networks
  • wavelet transform
  • weaning
  • mechanical ventilation

Líneas de Investigación UNAB

  • Eficiencia energética en procesos y operaciones insdustriales

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