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Assessment of Obstructive Sleep Apnea Severity Using Audio-based Snoring Features by Jiali Xie; Pedro Fonseca; Johannes van Dijk; Sebastiaan Overeem; Xi Long is a Medicine article available to read on EtoBox.

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Background and Objective Snoring is a prima symptom of obstructive sleep apnea (OSA). Here, we add audio-based snoring features to improve the non-obtrusive assessment of sleep apnea, by estimating the apnea-hypopnea index (AHI) and classifying OSA severity. Methods We propose novel features to quantify temporal changes between snores (snore rate variability) and to describe trends in snore energy, based on the assessment of snore sounds from audio signals over the full night. We then combined those features with age, body mass index (BMI) and features described in literature. An extreme gradient boosting algorithm was trained with all these features on AHI estimation. The estimated AHI was then used to classify OSA severity. Results Audio-based estimated AHI showed a significant Spearman's correlation with the AHI based on gold-standard polysomnography (R = 0.786, P < 0.0001). Our results outperformed a model trained with solely previously described features in our dataset (R = 0.676, P < 0.0001) and a model trained with the combination of previously described features, age, and BMI (R = 0.731, P < 0.0001). The mean absolute error of AHI estimation was 7.26 events/h. Area under th

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Author

Jiali Xie; Pedro Fonseca; Johannes van Dijk; Sebastiaan Overeem; Xi Long

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)