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 |
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01.09.2023
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| ISSN: | 1746-8094, 1746-8108 |
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| Abstract | •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 estimates of the apnea-hypopnea index showed a significant correlation with the polysomnographic gold standard, outperforming the model with the baseline feature set from a previous study.
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.
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.
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 the receiver operating characteristic curve outcomes were 0.90, 0.87 and 0.93 for classifying patients with varying severity separated by the canonical thresholds of 5, 15 and 30 events/h respectively. The accuracy of classifying subjects to four classes (no, mild, moderate, and severe OSA) was 59.3 %.
Additional audio-based snore features can improve the performance of non-obtrusive AHI estimation and OSA severity classification methods. |
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| AbstractList | •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 estimates of the apnea-hypopnea index showed a significant correlation with the polysomnographic gold standard, outperforming the model with the baseline feature set from a previous study.
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.
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.
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 the receiver operating characteristic curve outcomes were 0.90, 0.87 and 0.93 for classifying patients with varying severity separated by the canonical thresholds of 5, 15 and 30 events/h respectively. The accuracy of classifying subjects to four classes (no, mild, moderate, and severe OSA) was 59.3 %.
Additional audio-based snore features can improve the performance of non-obtrusive AHI estimation and OSA severity classification methods. |
| ArticleNumber | 104942 |
| Author | Overeem, Sebastiaan Long, Xi Xie, Jiali Fonseca, Pedro van Dijk, Johannes |
| Author_xml | – sequence: 1 givenname: Jiali surname: Xie fullname: Xie, Jiali organization: Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands – sequence: 2 givenname: Pedro surname: Fonseca fullname: Fonseca, Pedro organization: Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands – sequence: 3 givenname: Johannes surname: van Dijk fullname: van Dijk, Johannes organization: Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands – sequence: 4 givenname: Sebastiaan surname: Overeem fullname: Overeem, Sebastiaan organization: Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands – sequence: 5 givenname: Xi surname: Long fullname: Long, Xi email: x.long@tue.nl organization: Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands |
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| Keywords | OSA classification AHI Audio signal processing Sound features Apnea-hypopnea index Machine learning Sleep apnea Sleep disordered breathing Snoring |
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