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

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; neural networks; wavelet transform; weaning.

Figure 1

Excerpt of ECG and respiratory flow signals from patients undergoing extubation process of (a,b) a patient from the successful group, (c,d) a patient from the failed group, and (e,f) a patient from the reintubated group.

Figure 2

Excerpt of breathing duration time series ($T_{Tot}$) of a patient from (a) successful group (SG), (b) failed group (FG), and (c) reintubated group (RG).

Figure 3

Excerpt of beat-to-beat interval ($\mathrm{RR}$) from a patient of (a) successful group (SG), (b) failed group (FG), and (c) reintubated group (RG).

Figure 4

Schematic representation of the 32 variables extracted from the power spectral density for each of the 8 time series that describe the respiratory and cardiac pattern. $T_I$: inspiratory time, $T_E$ : expiratory time, $T_{Tot}$ : duration of the respiratory cycle, $V_T$ : tidal volume, $T_I/T_{Tot}$ : inspiratory fraction, $V_T/T_I$ : mean inspired flow, $f/V_T$ : frequency–tidal volume ratio, where $f$ is the respiratory rate; RR: cardiac beat-to-beat interval.

Figure 5

Algorithm of DWT decomposition through filter bank, where X[n] is the input signal, AC1, DC1 and AC2, DC2 represent two levels of decomposition of X[n], respectively.