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

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.