Assessment of obstructive sleep apnea severity using audio-based snoring features

•Novel audio-based features that exploit snoring characteristics including the variability of snoring sounds and trend in snore energy were proposed.•An extreme gradient boosting model was trained for apnea-hypopnea index estimation with a medium real-world clinical OSA population.•Audio-based estim...

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Veröffentlicht in:	Biomedical signal processing and control Jg. 86; S. 104942
Hauptverfasser:	Xie, Jiali, Fonseca, Pedro, van Dijk, Johannes, Overeem, Sebastiaan, Long, Xi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2023
Schlagworte:	AHI Apnea-hypopnea index Audio signal processing Machine learning OSA classification Sleep apnea Sleep disordered breathing Snoring Sound features OSA classification AHI Audio signal processing Sound features Apnea-hypopnea index Machine learning Sleep apnea Sleep disordered breathing Snoring
ISSN:	1746-8094, 1746-8108
Online-Zugang:	Volltext
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