P137 Deep learning enables accurate automatic sleep stage classification in a clinical paediatric population

INTRODUCTION: Sleep disorders are widespread in children and associated with a myriad of detrimental health sequelae. Accurate identification of sleep stages is crucial in diagnosing various sleep disorders; however, manual sleep stage scoring can be subjective, laborious, and costly. To tackle these shortcomings, we aimed to develop an accurate deep learning-based approach to automate sleep staging in a paediatric cohort. METHODS: A clinical dataset (n=115, 35% girls) containing overnight polysomnographic recordings of 10–13-year-old Icelandic children from the EuroPrevall-iFAAM study was utilised to develop a combined convolutional and long short-term memory neural network architecture. A three-channel input comprising electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography was used to train and evaluate the model to classify sleep into five stages (wake/N1/N2/N3/REM) using 10-fold cross-validation. Further, inter-rater reliabilities between two manual scorers and the automatic method were investigated in a subset (n=10) of the population. RESULTS: The automatic classification model achieved an accuracy of 84.5% (Cohen’s kappa κ=0.78: substantial agreement with manual scorings). Inter-rater reliability attained between two manual scorers was 84.6% (κ=0.78), and the automatic method achieved similar concordances with them, 83.4% (κ=0.76) and 82.7% (κ=0.75). DISCUSSION: The developed model achieved high accuracy and compared favourably to previously published state-of-the-art methods (performance range: 74.8%-84.3%). Inter-rater reliabilities were on par with the consensus between manual scorers and even better than among international sleep centres (commonly 0.57–0.63 as per literature). Therefore, incorporating the proposed methodology in clinical practice could be highly beneficial as it enables fast, cost-effective, and accurate sleep classification in children.