Medical Support System for Spontaneous Breathing Trial Prediction Using Nonuniform Discrete Fourier Transform

Hernando González; Carlos Julio Arizmendi; Beatriz F. Giraldo

doi:10.3991/ijoe.v20i16.52859

Authors

Hernando González Universidad Autónoma de Bucaramanga, Bucaramanga, Colombia
Carlos Julio Arizmendi Universidad Autónoma de Bucaramanga, Bucaramanga, Colombia
Beatriz F. Giraldo Universitat Politècnica de Catalunya – Barcelona Tech (UPC), Barcelona, Spain; Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain ; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Spain

DOI:

https://doi.org/10.3991/ijoe.v20i16.52859

Keywords:

Spontaneous breathing trial, weaning, nonuniform discrete Fourier transform

Abstract

Spontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients’ readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success or failure of SBT remains a significant challenge in clinical practice. This study proposes a healthcare system that employs machine learning techniques to predict the outcome of SBT. The model is trained on respiratory flow and electrocardiogram (ECG) signals, employing the non-uniform discrete Fourier transform (NUDFT) for frequency domain analysis. The SBT prediction model has the potential to significantly enhance clinical decision-making by enabling the early identification of patients at risk for SBT failure, achieving an accuracy of 84.4 ± 3.2%.