Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series

Abstract

AbstractNeonatal apneas and hypopneas present a serious risk for healthy infant development. Treating these adverse events requires frequent manual stimulation by skilled personnel, which can lead to alert fatigue. Automatically predicting these adverse events before they occur would enable the use of methods for automatic intervention. In this work, we propose a neural additive model to predict individual events of neonatal apnea and hypopnea and apply it to a physiological dataset from infants with Robin sequence at risk of upper airway obstruction. The dataset will be made publicly available together with this study. Our model achieved an average area under the receiver operating characteristic curve of 0.80 by additively combining information from different modalities of the respiratory polygraphy recording. This permits the prediction of individual apneas and hypopneas up to 15 seconds before they occur. Its additive nature makes the model inherently interpretable, which allowed insights into how important a given signal modality is for prediction and which patterns in the signal are discriminative. For our problem of predicting apneas and hyponeas in infants with Robin sequence, prior irregularities in breathing-related modalities as well as decreases in SpO2levels were especially discriminative.