Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach.

Abstract

Sleep apnea in children is a major health problem affecting one to five percent of children (in the US). If not treated in a timely manner, it can also lead to other physical and mental health issues. Pediatric sleep apnea has different clinical causes and characteristics than adults. Despite a large group of studies dedicated to studying adult apnea, pediatric sleep apnea has been studied in a much less limited fashion. Relatedly, at-home sleep apnea testing tools and algorithmic methods for automatic detection of sleep apnea are widely present for adults, but not children. In this study, we target this gap by presenting a machine learning-based model for detecting apnea events from commonly collected sleep signals. We show that our method outperforms state-of-the-art methods across two public datasets, as determined by the F1-score and AUROC measures. Additionally, we show that using two of the signals that are easier to collect at home (ECG and SpO₂) can also achieve very competitive results, potentially addressing the concerns about collecting various sleep signals from children outside the clinic. Therefore, our study can greatly inform ongoing progress toward increasing the accessibility of pediatric sleep apnea testing and improving the timeliness of the treatment interventions.