Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals

TL;DR

This study uses machine learning to identify EEG biomarkers associated with sleep oxygen desaturations in pediatric sleep apnea, achieving around 67% accuracy and suggesting potential for non-invasive diagnosis.

Contribution

The paper introduces a machine learning approach to detect oxygen desaturation biomarkers from EEG signals, including latent signals from non-desaturation periods, advancing sleep apnea diagnostics.

Findings

Achieved 66.8% balanced accuracy in classifying desaturation events.

Identified potential EEG biomarkers for oxygen desaturation.

Demonstrated the ability to detect desaturation-related signals even outside events.

Abstract

In this work, we leverage machine learning techniques to identify potential biomarkers of oxygen desaturation during sleep exclusively from electroencephalogram (EEG) signals in pediatric patients with sleep apnea. Development of a machine learning technique which can successfully identify EEG signals from patients with sleep apnea as well as identify latent EEG signals which come from subjects who experience oxygen desaturations but do not themselves occur during oxygen desaturation events would provide a strong step towards developing a brain-based biomarker for sleep apnea in order to aid with easier diagnosis of this disease. We leverage a large corpus of data, and show that machine learning enables us to classify EEG signals as occurring during oxygen desaturations or not occurring during oxygen desaturations with an average 66.8% balanced accuracy. We furthermore investigate the ability of machine learning models to identify subjects who experience oxygen desaturations from EEG data that does not occur during oxygen desaturations. We conclude that there is a potential biomarker for oxygen desaturation in EEG data.