Recurrent neural network for classification of snoring and non-snoring sound events

Bruno Arsenali, Johannes van Dijk, Okke Ouweltjes, Bert den Brinker, Dirk Pevernagie, Roy Krijn, Merel van Gilst, Sebastiaan Overeem

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)

Samenvatting

Obstructive sleep apnea (OSA) is a disorder that affects up to 38% of the western population. It is characterized by repetitive episodes of partial or complete collapse of the upper airway during sleep. These episodes are almost always accompanied by loud snoring. Questionnaires such as STOP-BANG exploit snoring to screen for OSA. However, they are not quantitative and thus do not exploit its full potential. A method for automatic detection of snoring in whole-night recordings is required to enable its quantitative evaluation. In this study, we propose such a method. The centerpiece of the proposed method is a recurrent neural network for modeling of sequential data with variable length. Mel-frequency cepstral coefficients, which were extracted from snoring and non-snoring sound events, were used as inputs to the proposed network. A total of 20 subjects referred to clinical sleep recording were also recorded by a microphone that was placed 70 cm from the top end of the bed. These recordings were used to assess the performance of the proposed method. When it comes to the detection of snoring events, our results show that the proposed method has an accuracy of 95%, sensitivity of 92%, and specificity of 98%. In conclusion, our results suggest that the proposed method may improve the process of snoring detection and with that the process of OSA screening. Follow-up clinical studies are required to confirm this potential.

Originele taal-2Engels
Titel40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's328-331
Aantal pagina's4
ISBN van elektronische versie978-1-5386-3646-6
ISBN van geprinte versie978-1-5386-3647-3
DOI's
StatusGepubliceerd - 26 okt 2018
Evenement40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society= (EMBC 2018) - Hawaii Convention Center, Honolulu, Verenigde Staten van Amerika
Duur: 18 jul 201821 jul 2018
Congresnummer: 40
https://embc.embs.org/2018/

Congres

Congres40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society= (EMBC 2018)
Verkorte titelEMBC 2018
LandVerenigde Staten van Amerika
StadHonolulu
Periode18/07/1821/07/18
Internet adres

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Citeer dit

Arsenali, B., van Dijk, J., Ouweltjes, O., den Brinker, B., Pevernagie, D., Krijn, R., ... Overeem, S. (2018). Recurrent neural network for classification of snoring and non-snoring sound events. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (blz. 328-331). [8512251] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2018.8512251