ArticleComputer methods and programs in biomedicine2021
Predicting apneic events in preterm infants using cardio-respiratory and movement features.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series.PLOS digital health · 2024Article
- Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study.BMC pediatrics · 2024Article
- Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.Interactive journal of medical research · 2024Article
- A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database.Frontiers in pediatrics · 2024Article
- Bradycardia May Decrease Cardiorespiratory Coupling in Preterm Infants.Entropy (Basel, Switzerland) · 2023Article
- Review
- Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification.Frontiers in pediatrics · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
objectivePreterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events.
methodsFollowing previous studies showing that respiratory instabilities are closely associated with bouts of movement, we present a modeling framework that can predict ABH events using both movement and cardio-respiratory features derived from routine clinical recordings. In 10 preterm infants, movement onsets and durations were estimated with a wavelet-based algorithm that quantified artifactual distortions of the photoplethysmogram signal. For prediction, cardio-respiratory features were created from time-delayed correlations of inter-beat and inter-breath intervals with past values; movement features were derived from time-delayed correlations with inter-breath intervals. Gaussian Mixture Models and Logistic Regression were used to develop predictive models of apneic events. Performance of the models was evaluated with ROC curves.
resultsPerformance of the prediction framework (mean AUC) was 0.77 ± 0.04 for 66 ABH events on training data from 7 infants. When grouped by the severity of the associated bradycardia during the ABH event, the framework was able to predict 83% and 75% of the most severe episodes in the 7-infant training set and 3-infant test set, respectively. Notably, inclusion of movement features significantly improved the predictions compared with modeling with only cardio-respiratory signals.
conclusionsOur findings suggest that recordings of movement provide important information for predicting ABH events in preterm infants, and can inform preemptive interventions designed to reduce the incidence and severity of ABH events.
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