Possibilities of diagnosing breathing disorders during sleep in home conditions
| 1 | Instytut Podstaw Budowy Maszyn Politechnika Warszawska |
| 2 | Faculty of Sleep Apnea and Snoring, MML Medical Center |
CORRESPONDING AUTHOR
Submission date: 2021-08-12
Final revision date: 2021-12-11
Acceptance date: 2021-12-12
Online publication date: 2021-12-14
Publication date: 2021-12-14
Diagnostyka 2021;22(4):115–121
KEYWORDS
TOPICS
ABSTRACT
Disorders of breathing during sleep not only adversely affect the condition of the body during the daytime, but, above all, can be dangerous to health and life. Clinical methods of diagnosing these disorders are highly developed and, as a result, allow to effectively eliminate the problem, but still the problem is early diagnosis at home, which will be the basis for reporting to the doctor for extended examinations.
This paper presents a proposed algorithm for inferring sleep-disordered breathing supported by conclusions from work on investigating the associations of discriminants with selected fragments of acoustic signals. The effectiveness of the developed algorithm was verified on a test sample of acoustic signals from selected patients treated by the MML clinic. The results of the study are the basis for the development of a numerical application for preclinical diagnosis of sleep apnea and sleep-disordered breathing. The verification of the algorithm carried out on real examples confirms the correctness of the assumptions made, demonstrates its effectiveness and suitability for use in a mobile application.
REFERENCES (16)
1.
Weber S, Badr S. The Occurrence of Sleep-Disordered Breathing among Middle-Aged Adults. New England Journal of Medicine 1993; 328(17): 1230-1235. https://doi.org/10.1056/nejm19....
2.
Arnaud C, Bochaton T, Pépin J, Belaidi E. Obstructive sleep apnoea and cardiovascular consequences: Pathophysiological mechanisms. Archives of Cardiovascular Diseases 2020; 113(5): 350-358. https://dx.doi.org/10.1016/j.a....
3.
Chang HP, Chen YF, Du JK. Obstructive sleep apnea treatment in adults. Kaohsiung Journal of Medical Sciences 2020; 36(1): 7-12. https://doi.org/10.1002/kjm2.1....
4.
Yaggi H, Concato J, Kernan W, Lichtman J, Brass L, Mohsenin V. Obstructive Sleep Apnea as a Risk Factor for Stroke and Death. New England Journal of Medicine 2005; 353(19): 2034-2041. https://doi.org/10.1056/nejmoa....
5.
Mencar C, Gallo C, Mantero M, Tarsia P, Carpagnano G, Foschino Barbaro M, Lacedonia D. Application of machine learning to predict obstructive sleep apnea syndrome severity. Health Informatics Journal 2020; 26(1): 298-317. https://dx.doi.org/10.1177/146....
6.
Cho S. W, Wee JH, Yoo S, Heo E, Ryu B, Kim Y, Lee JS, Kim J. W. Effect of lifestyle modification using a smartphone application on obesity with obstructive sleep apnea: A short-term, randomized controlled study. Clinical and Experimental Otorhinolaryngology 2018; 1(3): 192-198. https://doi.org/10.21053/ceo.2....
7.
Koo SK, Kwon SB, Koh TK, Ji CL, Park, GH, Lee HB. Acoustic analyses of snoring sounds using a smartphone in patients undergoing septoplasty and turbinoplasty. European Archives of Oto-Rhino-Laryngology 2021; 278(1):257-263. https://doi.org/10.1007/s00405....
8.
Wszołek W, Izworski A, Izworski G. Signal processing and analysis of pathological speech using artificial intelligence and learning systems methods. Acta Physica Polonica A 2013; 123(6): 995-1000. http://dx.doi.org/10.12693/APh....
9.
Wszołek W, Kłaczyński M. Comparative study of the selected methods of laryngeal tone determination. Archives of Acoustics 2006; 31(4): 219-226.
10.
Kłaczyński M. Vibroacoustic methods in diagnosis of selected laryngeal diseases. Journal of Vibroengineering 2015; 17(4): 2089-2098.
11.
Klekot G. Indicator of vibroacoustic energy propagation as a selection criterion of design solution. Archives of Acoustics 2013; 38(4): 489-494. https://doi.org/10.2478/aoa-20....
12.
Górnicka D, Klekot G, Michalik M. Acoustic signal as a carrier of information on breathing sleep disorders. Vibrations in Physical Systems 2019; 30(2).
13.
Pevernagie D, Aarts R. M, de Meyer M. The acoustics of snoring. Sleep Medicine Reviews 2010: 14(2): 131-144. https://doi.org/10.1016/j.smrv....
14.
Levartovsky A, Dafna E, Zigel Y, Tarasiuk A. Breathing and snoring sound characteristics during sleep in adults. Journal of Clinical Sleep Medicine 2016; 12(3): 375-384. https://dx.doi.org/10.5664/jcs....
15.
Klekot G, Michalik M. An analysis of a possibility to use vibroacoustic methods for the diagnosis of the sleep apnea syndrome. Applied Condition Monitoring 2018; 10: 477-485. https://doi.org/10.1007/978-3-....
16.
Górnicka D, Klekot G, Michalik M. Examinations of acoustic signals of patients having snoring problem. Journal of Vibroengineering 2017; 19(7): 5553-5559. https://doi.org/10.21595/jve.2....
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