Archives of Acoustics, 50, 1, pp. 69-79, 2025
10.24425/aoa.2025.153650

Snoring Sounds Classification of OSAHS Patients Based on Model Fusion

Yexin LUO
School of Physics and Optoelectronics, South China University of Technology
China

Jianxin PENG
School of Physics and Optoelectronics, South China University of Technology
China

Li DING
School of Advanced Manufacturing Engineering, Hefei University
China

Yikai ZHANG
School of Physics and Optoelectronics, South China University of Technology
China

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

Qianfan ZHANG
School of Physics and Optoelectronics, South China University of Technology
China

Houpeng CHEN
School of Physics and Optoelectronics, South China University of Technology
China

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a prevalent and detrimental chronic condition. The conventional diagnostic approach for OSAHS is intricate and costly. Snoring is one of the most typical and easily obtained symptom of OSAHS patients. In this study, a series of acoustic features are extracted from snoring sounds. A fused model that integrates a deep neural network, K-nearest neighbors (KNN), and a random under sampling boost algorithm is proposed to classify snoring sounds of simple snorers (SSSS), simple snoring sounds of OSAHS patients (SSSP), and apnea-hypopnea snoring sounds of OSAHS patients (APSP). The ReliefF algorithm is employed to select features with high relevance in each classification model. A hard voting strategy is implemented to obtain an optimal fused model. Results show that the proposed fused model achieves commendable performance with an accuracy rate of 85.76 %. It demonstrates the effectiveness and validity of assisting in diagnosing OSAHS patients based on the analysis of snoring sounds.
Keywords: obstructive sleep apnea hypopnea syndrome; snoring sounds; deep neural network; model fusion
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Copyright © 2025 The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.

References

Alshaer H., Hummel R., Mendelson M., Marshal T., Bradley T.D. (2019), Objective relationship between sleep apnea and frequency of snoring assessed by machine learning, Journal of Clinical Sleep Medicine, 15(3): 463–470, https://doi.org/10.5664/jcsm.7676.

Azarbarzin A., Moussavi Z. (2013), Snoring sounds variability as a signature of obstructive sleep apnea, Medical Engineering and Physics, 35(4): 479–485, https://doi.org/10.1016/j.medengphy.2012.06.013.

Berry R.B. et al. (2012), Rules for scoring respiratory events in sleep: Update of the 2007 AASM manual for the scoring of sleep and associated events, Journal of Clinical Sleep Medicine, 8(5): 597–619, https://doi.org/10.5664/jcsm.2172.

Caron C.J.J.M. et al. (2017), Obstructive sleep apnoea in craniofacial microsomia: Analysis of 755 patients, International Journal of Oral and Maxillofacial Surgery, 46(10): 1330–1337, https://doi.org/10.1016/j.ijom.2017.05.020.

Castillo-Escario Y., Ferrer-Lluis I., Montserrat J.M., Jane R. (2019), Entropy analysis of acoustic signals recorded with a smartphone for detecting apneas and hypopneas: A comparison with a commercial system for home sleep apnea diagnosis, IEEE Access, 7: 128224–128241, https://doi.org/10.1109/ACCESS.2019.2939749.

Cheng S. et al. (2022), Automated sleep apnea detection in snoring signal using long short-term memory neural networks, Biomedical Signal Processing and Control, 71(Part B): 103238, https://doi.org/10.1016/j.bspc.2021.103238.

Ding L., Peng J., Song L., Zhang X. (2023), Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM, Biomedical Signal Processing and Control, 80(Part 2): 104351, https://doi.org/10.1016/j.bspc.2022.104351.

Ding L., Peng J., Song L., Zhang X. (2024), Automatically detecting OSAHS patients based on transfer learning and model fusion, Physiological Measurement, 45(5): 055013, https://doi.org/10.1088/1361-6579/ad4953.

Eckert D.J., Jordan A.S., Merchia P., Malhotra A. (2007), Central sleep apnea: Pathophysiology and treatment, Chest, 131(2): 595–607, https://doi.org/10.1378/chest.06.2287.

Fiz J.A. et al. (1996), Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea, European Respiratory Journal, 9(11): 2365–2370, https://doi.org/10.1183/09031936.96.09112365.

Friedman M., Ibrahim H., Joseph N.J. (2004), Staging of obstructive sleep apnea/hypopnea syndrome: A guide to appropriate treatment, Laryngoscope, 114(3): 454–459, https://doi.org/10.1097/00005537-200403000-00013.

Ghosh P., Varma N.K.S., Ajith V.V., Suresh A. (2021), Upper airway and its association with neck circumference and hyoid position in OSA subjects – A cephalometric study, International Journal of Current Research and Review, 13(6): 167–171, https://doi.org/10.31782/IJCRR.2021.13610.

Gislason T., Benediktsdottir B. (1995), Snoring, apneic episodes, and nocturnal hypoxemia among children 6 months to 6 years old: An epidemiologic study of lower limit of prevalence, Chest, 107(4): 963–966, https://doi.org/10.1378/chest.107.4.963.

Gottlieb D.J., Punjabi N.M. (2020), Diagnosis and management of obstructive sleep apnea: A review, Journal of the American Medical Association, 323(14): 1389–1400, https://doi.org/10.1001/jama.2020.3514.

Herzog M., Schmidt A., Bremert T., Herzog B., Hosemann W., Kaftan H. (2008), Analysed snoring sounds correlate to obstructive sleep disordered breathing, European Archives of Oto-Rhino-Laryngology, 265(1): 105–113, https://doi.org/10.1007/s00405-007-0408-8.

Hou L., Zhang W., Shi D., Liu H. (2019), Estimation of apnea hypopnea index based on acoustic features of snoring, Journal of Shanghai University (Natural Science), 25(4): 435–444, https://doi.org/10.12066/j.issn.1007-2861.1942.

Izci B., Douglas N.J. (2012), Obstructive sleep apnea-hypopnea syndrome, [in:] Obstructive Sleep Apnea: Causes, Treatment and Health Implications, Sacchetti L.M., Mangiardi P. [Eds.], pp. 129–182, Nova Science Publishers.

Janiesch C., Zschech P., Heinrich K. (2021), Machine learning and deep learning, Electronic Markets, 31(3): 685–695, https://doi.org/10.1007/s12525-021-00475-2.

Karunajeewa A.S., Abeyratne U.R., Hukins C. (2011), Multi-feature snore sound analysis in obstructive sleep apnea–hypopnea syndrome, Physiological Measurement, 32(1): 83, https://doi.org/10.1088/0967-3334/32/1/006.

Korompili G. et al. (2021), PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies, Scientific Data, 8(1): 197, https://doi.org/10.1038/s41597-021-00977-w.

Kursa M.B., Rudnicki W.R. (2010), Feature selection with the Boruta package, Journal of Statistical Software, 36(11): 1–13, https://doi.org/10.18637/jss.v036.i11.

Lee B.S., Ellis D.P.W. (2012), Noise robust pitch tracking by subband autocorrelation classification, [in:] 13th Annual Conference of the International Speech Communication Association 2012, https://doi.org/10.21437/interspeech.2012-221.

Li J. et al. (2017), Feature selection: A data perspective, ACM Computing Surveys, 50(6): 94, https://doi.org/10.1145/3136625.

Lugaresi E., Cirignotta F., Montagna P. (1988), Pathogenic aspects of snoring and obstructive apnea syndrome, Schweizerische Medizinische Wochenschrift, 118(38).

Markandeya M.N., Abeyratne U.R., Hukins C. (2018), Characterisation of upper airway obstructions using wide-band snoring sounds, Biomedical Signal Processing and Control, 46: 201–211, https://doi.org/10.1016/j.bspc.2018.07.013.

Minaritzoglou A., Vagiakis E. (2008), Polysomnography: Recent data on procedure and analysis, Pneuomon, 21(4).

Osman A.M., Carter S.G., Carberry J.C., Eckert D.J. (2018), Obstructive sleep apnea: Current perspectives, Nature and Science of Sleep, 10: 21–34, https://doi.org/10.2147/NSS.S124657.

Perez-Padilla J.R., Slawinski E., Difrancesco L.M., Feige R.R., Remmers J.E., Whitelaw W.A. (1993), Characteristics of the snoring noise in patients with and without occlusive sleep apnea, American Review of Respiratory Disease, 147(3), https://doi.org/10.1164/ajrccm/147.3.635.

Pevernagie D., Aarts R.M., De Meyer M. (2010), The acoustics of snoring, Sleep Medicine Reviews, 14(2): 131–144, https://doi.org/10.1016/j.smrv.2009.06.002.

Qian K. et al. (2021), Can machine learning assist locating the excitation of snore sound? A review, IEEE Journal of Biomedical and Health Informatics, 25(4): 1233–1246, https://doi.org/10.1109/JBHI.2020.3012666.

Qian K., Janott C., Zhang Z., Heiser C., Schuller B. (2016), Wavelet features for classification of vote snore sounds, [in:] 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 221–225, https://doi.org/10.1109/ICASSP.2016.7471669.

Redline S. et al. (2010), Obstructive sleep apnea–hypopnea and incident stroke, American Journal of Respiratory and Critical Care Medicine, 182(2), https://doi.org/10.1164/rccm.200911-1746oc.

Seiffert C., Khoshgoftaar T.M., Van Hulse J., Napolitano A. (2010), RUSBoost: A hybrid approach to alleviating class imbalance, [in:] IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 40(1): 185–197, https://doi.org/10.1109/TSMCA.2009.2029559.

Shen F., Cheng S., Li Z., Yue K., Li W., Dai L. (2020), Detection of snore from OSAHS patients based on deep learning, Journal of Healthcare Engineering, https://doi.org/10.1155/2020/8864863.

Sola-Soler J., Jane R., Fiz J.A., Morera J. (2007), Automatic classification of subjects with and without Sleep Apnea through snoring analysis, [in:] 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, https://doi.org/10.1109/IEMBS.2007.4353739.

Song Y., Sun X., Ding L., Peng J., Song L., Zhang X. (2023), AHI estimation of OSAHS patients based on snoring classification and fusion model, American Journal of Otolaryngology, 44(5): 103964, https://doi.org/10.1016/j.amjoto.2023.103964.

Sowho M., Sgambati F., Guzman M., Schneider H., Schwartz A. (2020), Snoring: A source of noise pollution and sleep apnea predictor, Sleep, 43(6), https://doi.org/10.1093/sleep/zsz305.

Sun X., Ding L., Song Y., Peng J., Song L., Zhang X. (2023), Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models, Physiological Measurement, 44(4): 045003, https://doi.org/10.1088/1361-6579/accd43.

Sun X., Peng J., Zhang X., Song L. (2022), Effective feature selection based on Fisher Ratio for snoring recognition using different validation methods, Applied Acoustics, 185: 108429, https://doi.org/10.1016/j.apacoust.2021.108429.

Ulualp S.O. (2010), Snoring and obstructive sleep apnea, Medical Clinics of North America, 94(5): 1047–1055, https://doi.org/10.1016/j.mcna.2010.05.002.

Wang C., Peng J., Song L., Zhang X. (2017), Automatic snoring sounds detection from sleep sounds via multi-features analysis, Australasian Physical and Engineering Sciences in Medicine, 40(1): 127–135, https://doi.org/10.1007/s13246-016-0507-1.

White D.P. (2005), Pathogenesis of obstructive and central sleep apnea, American Journal of Respiratory and Critical Care Medicine, 172(11), https://doi.org/10.1164/rccm.200412-1631SO.

Wu Z., Wang X., Jiang B. (2020), Fault diagnosis for wind turbines based on ReliefF and eXtreme gradient boosting, Applied Sciences, 10(9): 3258, https://doi.org/10.3390/app10093258.

Zhang S., Li X., Zong M., Zhu X., Cheng D. (2017), Learning k for kNN Classification, ACM Transactions on Intelligent Systems and Technology, 8(3): 43, https://doi.org/10.1145/2990508.

Zheng F., Zhang G., Song Z. (2001), Comparison of different implementations of MFCC, Journal of Computer Science and Technology, 16(6): 582–589, https://doi.org/10.1007/BF02943243.




DOI: 10.24425/aoa.2025.153650