Sleep Snoring Sound Recognition Based on Wavelet Packet Transform

• Autorzy: Ding Li , Peng Jianxin , Zhang Xiaowen , Song Lijuan

Identyfikatory

DOI

10.24425/aoa.2022.142906

• Języki publikacji: EN

Abstrakty

EN

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.

Słowa kluczowe

EN

snoring recognition; wavelet packet transform; feature selection; machine learning

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2023
• Tom: Vol. 48, No. 1
• Strony: 3--12
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Ding Li

• School of Physics and Optoelectronics, South China University of Technology Guangzhou, China

• https://orcid.org/0000-0002-3465-3857

autor

Peng Jianxin

• School of Physics and Optoelectronics, South China University of Technology Guangzhou, China

• https://orcid.org/0000-0002-3985-5802

autor

Zhang Xiaowen

• State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University Guangzhou, China

autor

Song Lijuan

• State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University Guangzhou, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL).