A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Abstract
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.Keywords:
acoustical analysis, feature extraction, support vector machine, snoring soundReferences
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2. Becker H.F. et al. (1999), Breathing during sleep in patients with nocturnal desaturation, American Journal of Respiratory and Critical Care Medicine, 159(1): 112–118, https://doi.org/10.1164/ajrccm.159.1.9803037
3. Ben-Israel N., Tarasiuk A., Zigel Y. (2012), Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults, Sleep, 35(9): 1299–305, https://doi.org/10.5665/sleep.2092
4. Cortes C, Vapnik V.N. (1995), Support vector networks, Machine Learning, 20(3): 273–297, https://doi.org/10.1007/BF00994018
5. Emoto T., Abeyratne U.R., Kawano K., Okada T., Jinnouchi O., Kawata I. (2018), Detection of sleep breathing sound based on artificial neural network analysis, Biomedical Signal Processing and Control, 41, 81–89, https://doi.org/10.1016/j.bspc.2017.11.005
6. FitzGerald D., Paulus J. (2006), Unpitched percussion transcription, [in:] A. Klapuri, M. Davy [Eds], Signal processing methods for music transcription, Springer, Boston, MA, https://doi.org/10.1007/0-387-32845-9_5
7. Fiz J.A. et al. (1996), Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnea, European Respiratory Journal, 9(11): 2365–2370, https://doi.org/10.1183/09031936.96.09112365
8. Flemons W.W., Tsai W. (1997), Quality of life consequences of sleep-disordered breathing, Journal of Allergy and Clinical Immunology, 99(2): S750–S756, https://doi.org/10.1016/S0091-6749%2897%2970123-4
9. Ghaemmaghami H., Abeyratne U.R., Hukins C. (2009), Normal probability testing of snore signals for diagnosis of obstructive sleep apnea, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, pp. 5551–5554, https://doi.org/10.1109/IEMBS.2009.5333733
10. Hirotaka H., Masakazu T., Syunsuke T., Toshikazu S., Hiroshi Y. (2017), Validation of a new snoring detection device based on a hysteresis extraction algorithm, Auris Nasus Larynx, 44(5): 576–582, https://doi.org/10.1016/j.anl.2016.12.009
11. Hrubos-Strøm H. et al. (2012), Sleep apnoea, anxiety, depression and somatoform pain: a community-based high-risk sample, European Respiratory Journal, 40(2): 400–407, https://doi.org/10.1183/09031936.00111411
12. Jin H. et al. (2015), Acoustic analysis of snoring in the diagnosis of obstructive sleep apnea syndrome: a call for more rigorous studies, Journal of Clinical Sleep Medicine, 11(7): 765–771, https://doi.org/10.5664/jcsm.4856
13. Kang J.H., Lin H.C. (2012), Obstructive sleep apnea and the risk of autoimmune diseases: a longitudinal population-based study, Sleep Medicine, 13(6): 583–588, https://doi.org/10.1016/j.sleep.2012.03.002
14. Kim T., Kim J. W., Lee K. (2018), Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques, Biomedical Engineering OnLine, 17(1): 16, https://doi.org/10.1186/s12938-018-0448-x
15. Loke Y.K., Brown J.W., Kwok C.S., Niruban A., Myint P.K. (2012), Association of obstructive sleep apnea with risk of serious cardiovascular events: a systematic review and meta-analysis, Circulation: Cardiovascular Quality and Outcomes, 5(5): 720–728, https://doi.org/10.1161/CIRCOUTCOMES.111.964783
16. Marin J.M. et al. (2012), Association between treated and untreated obstructive sleep apnea and risk of hypertension, JAMA, 307(20): 2169–2176, https://doi.org/10.1001/jama.2012.3418
17. Martínez-García M.A. et al. (2012), Cardiovascular mortality in obstructive sleep apnea in the elderly: role of long-term continuous positive airway pressure treatment: a prospective observational study, American Journal of Respiratory and Critical Care Medicine, 186(9): 909–916, https://doi.org/10.1164/rccm.201203-0448OC
18. Młyńczak M., Migacz E., Migacz M., Kukwa W. (2017), Detecting breathing and snoring episodes using a wireless tracheal sensor – a feasibility study, IEEE Journal of Biomedical and Health Informatics, 21(6): 1504–1510, https://doi.org/10.1109/JBHI.2016.2632976
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27. Richman J.S., Moorman J.R. (2000), Physiological time-series analysis using approximate entropy and sample entropy, American Journal of Physiology-Heart and Circulatory Physiology, 278(6): H2039–H2049, https://doi.org/10.1152/ajpheart.2000.278.6.H2039
28. Tobin M.J., Mador M.J., Guenther S.M., Lodato R.F., Sackner M.A. (1998), Variability of resting respiratory drive and timing in healthy-subjects, Journal of Applied Physiology, 65(1): 309–317, https://doi.org/10.1152/jappl.1988.65.1.309
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30. Xu H. et al. (2015), Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population, Sleep Breath, 19(2): 599–605, https://doi.org/10.1007/s11325-014-1055-0
31. Xu H., Yu L., Huang W., Chen L., He Y. (2009), A preliminary study of acoustic characteristics of snoring sound in patients with obstructive sleep apnea/ hypopnea syndrome (OSAHS) and with simple snoring, Journal of Audiology and Speech Pathology, 17(3): 235–238, http://en.cnki.com.cn/Article_en/CJFDTOTAL-TLXJ200903013.htm
