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Efficient snoring and breathing detection based on sub-band spectral statistics

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Sun, Xianga | Kim, Jin Younga; * | Won, Yonggwana | Kim, Jung-Jab | Kim, Kyung-Ahc

Affiliations: [a] Department of ECE, College of Engineering, Chonnam National University, Gwangju, 500-757, South Korea | [b] Department of Biomedical Engineering, College of Engineering, Chonbuk National University, Jeonju, South Korea | [c] Department of Biomedical Engineering, College of Medicine, Chonbuk National University, Jeonju, South Korea

Correspondence: [*] Address for correspondence: Jin Young Kim, Department of ECE, College of Engineering, Chonnam National University, Gwangju, 500-757, South Korea. Tel.: (+82)625301757; Fax: (+82)625301759; E-mail: [email protected].

Keywords: Snoring, acoustic analysis, obstructive sleep apnea, sub-band spectra

DOI: 10.3233/BME-151370

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S787-S793, 2015

Published: 2015
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Snoring, a common symptom in the general population may indicate the presence of obstructive sleep apnea (OSA). In order to detect snoring events in sleep sound recordings, a novel method was proposed in this paper. The proposed method operates by analyzing the acoustic characteristics of the snoring sounds. Based on these acoustic properties, the feature vectors are obtained using the mean and standard deviation of the sub-band spectral energy. A support vector machine is then applied to perform the frame-based classification procedure. This method was demonstrated experimentally to be effective for snoring detection. The database for detection included full-night audio recordings from four individuals who acknowledged having snoring habits. The performance of the proposed method was evaluated by classifying different events (snoring, breathing and silence) from the sleep sound recordings and comparing the classification against ground truth. The proposed algorithm was able to achieve an accuracy of 99.61% for detecting snoring events, 99.16% for breathing, and 99.55% for silence.

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