A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring

• Autorzy: Wang Can , Peng Jianxin , Zhang Xiaowen

Identyfikatory

DOI

10.24425/aoa.2020.132490

• Języki publikacji: EN

Abstrakty

EN

Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.

Słowa kluczowe

EN

acoustical analysis; feature extraction; support vector machine; snoring sound

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 1
• Strony: 141--151
• Opis fizyczny: Bibliogr. 31 poz., tab., wykr.

Twórcy

autor

Wang Can

• School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China

autor

Peng Jianxin

• School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China

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 510120, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).