Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

Figures

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  Figure 1. Biosignal data collecting and data analysis process. Biosignal data from patient monitor devices and parameters from mechanical ventilators were collected via our biosignal collecting system. Collected data were analyzed retrospectively to find out features for predicting weaning success.

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  Figure 2. Study overview. We selected biosignal-based features that showed differences between the weaning failure and success groups. Their usefulness was evaluated by applying these features to predict weaning success, followed by comparison with the pre-existing RSBI. ICU, intensive care unit; RSBI, rapid shallow breading index; SBT, spontaneous breathing trial.

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  Figure 3. Methods used for the feature extraction process. (A) Poincaré plot, (B) Sample entropy, and (C) Detrended fluctuation analysis. All three approaches evaluate the level of variability in time-series data. For numerical values, the Poincaré plot method was used; for waveform data, sample entropy and detrended fluctuation analysis methods were used.

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  Figure 4. Performance comparison between the model using RSBI alone and the model using RSBI and biosignal-based features. After 1000 iterations of bootstrapping, the mean AUROC and 1 standard deviation of each group are shown as a solid line and shaded area.

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  Figure 5. Feature importance in the Random Forest model to predict weaning success. All biosignal features were demonstrated to have value similar to RSBI.