Deep learning for sleep analysis on children with sleep-disordered breathing: automatic detection of mouth breathing events

Sturludóttir, Jóna Elísabet, Sigurðardóttir, Sigríður, Serwatko, Marta, Arnardóttir, Erna S., Hrubos-Strøm, Harald, Clausen, Michael Valur, Sigurðardóttir, Sigurveig, Óskarsdóttir, María and Islind, Anna Sigridur (2023) Deep learning for sleep analysis on children with sleep-disordered breathing: automatic detection of mouth breathing events. Frontiers in Sleep, 2, [1082996]. (doi:10.3389/frsle.2023.1082996).

Abstract

Introduction: Sleep-disordered breathing (SDB) can range from habitual snoring to severe obstructive sleep apnea (OSA). A common characteristic of SDB in children is mouth breathing, yet it is commonly overlooked and inconsistently diagnosed. The primary aim of this study is to construct a deep learning algorithm in order to automatically detect mouth breathing events in children from polysomnography (PSG) recordings. Methods: The PSG of 20 subjects aged 10–13 years were used, 15 of which had reported snoring or presented high snoring and/or high OSA values by scoring conducted by a sleep technologist, including mouth breathing events. The separately measured mouth and nasal pressure signals from the PSG were fed through convolutional neural networks to identify mouth breathing events. Results: The finalized model presented 93.5% accuracy, 97.8% precision, 89% true positive rate, and 2% false positive rate when applied to the validation data that was set aside from the training data. The model's performance decreased when applied to a second validation data set, indicating a need for a larger training set. Conclusion: The results show the potential of deep neural networks in the analysis and classification of biological signals, and illustrates the usefulness of machine learning in sleep analysis.

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Contributors

Author: María Óskarsdóttir

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