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Year 2021, Volume: 3 Issue: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering, 32 - 39, 13.01.2021

Abstract

References

  • 1. Kapoor, Mukesh, and Glen Greenough. "Home sleep tests for obstructive sleep apnea (OSA)." The Journal of the American Board of Family Medicine 28.4 (2015): 504-509.
  • 2. Mulgrew, Alan T., et al. "Diagnosis and initial management of obstructive sleep apnea without polysomnography: a randomized validation study." Annals of internal medicine 146.3 (2007): 157-166.
  • 3. Collop, Nancy A. "Portable monitoring for the diagnosis of obstructive sleep apnea." Current opinion in pulmonary medicine 14.6 (2008): 525-529.
  • 4. Shah, Parina, and Indira Gurubhagavatula. "Portable monitoring: practical aspects and case examples." Sleep Medicine Clinics 6.3 (2011): 355-366.
  • 5. Masa, Juan F., et al. "Effectiveness of sequential automatic-manual home respiratory polygraphy scoring." European Respiratory Journal 41.4 (2013): 879-887.
  • 6. Garg, Natasha, et al. "Home-based diagnosis of obstructive sleep apnea in an urban population." Journal of Clinical Sleep Medicine 10.8 (2014): 879-885.
  • 7. Tan, Hui-Leng, et al. "Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea." Sleep 37.2 (2014): 255-260.
  • 8. Pittman, Stephen D., et al. "Using a wrist-worn device based on peripheral arterial tonometry to diagnose obstructive sleep apnea: in-laboratory and ambulatory validation." Sleep 27.5 (2004): 923-933.
  • 9. Hedner, Jan, et al. "Sleep staging based on autonomic signals: a multi-center validation study." Journal of clinical sleep medicine (2011). 7:301– 6.
  • 10. Yalamanchali, Sreeya, et al. "Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis." JAMA Otolaryngology–Head & Neck Surgery 139.12 (2013): 1343-1350.
  • 11. Hairston, Ilana S. "The Use of WatchPAT™ for Home Sleep Testing Assessment of Sleep-Related Disordered Breathing (SDB) in Heart Disease Patients–Clinical & Operational Benefits."
  • 12. Yılmaz, Bülent, et al. "Sleep stage and obstructive apneaic epoch classification using single-lead ECG." Biomedical engineering online 9.1 (2010): 39.
  • 13. Babaeizadeh, Saeed, et al. "Automatic detection and quantification of sleep apnea using heart rate variability." Journal of electrocardiology 43.6 (2010): 535-541.
  • 14. Almazaydeh, Laiali, Khaled Elleithy, and Miad Faezipour. "Detection of obstructive sleep apnea through ECG signal features." 2012 IEEE International Conference on Electro/Information Technology. IEEE, 2012.
  • 15. Babaeizadeh, Saeed, et al. "Electrocardiogram-derived respiration in screening of sleep-disordered breathing." Journal of electrocardiology 44.6 (2011): 700-706.
  • 16. Gottlieb, Daniel J., and Naresh M. Punjabi. "Diagnosis and Management of Obstructive Sleep Apnea: A Review." Jama 323.14 (2020): 1389-1400.
  • 17. Hwang, Su Hwan, et al. "Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor." IEEE Transactions on Biomedical Engineering 61.7 (2014): 2125-2134.
  • 18. Penzel, Thomas, and AbdelKebir Sabil. "The use of tracheal sounds for the diagnosis of sleep apnoea." Breathe 13.2 (2017): e37-e45.
  • 19. Kalkbrenner, Christoph, et al. "Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders." Medical & biological engineering & computing 56.4 (2018): 671-681.
  • 20. Nakano, Hiroshi, Tomokazu Furukawa, and Takeshi Tanigawa. "Tracheal sound analysis using a deep neural network to detect sleep apnea." Journal of Clinical Sleep Medicine 15.8 (2019): 1125-1133.
  • 21. Duckitt, W. D., S. K. Tuomi, and T. R. Niesler. "Automatic detection, segmentation and assessment of snoring from ambient acoustic data." Physiological measurement 27.10 (2006): 1047.
  • 22. Karunajeewa, Asela S., Udantha R. Abeyratne, and Craig Hukins. "Multi-feature snore sound analysis in obstructive sleep apnea–hypopnea syndrome." Physiological measurement 32.1 (2010): 83.
  • 23. Almazaydeh, Laiali, et al. "Apnea detection based on respiratory signal classification." Procedia Computer Science 21 (2013): 310-316.
  • 24. Baboli, Mehran, et al. "Wireless Sleep Apnea Detection Using Continuous Wave Quadrature Doppler Radar." IEEE Sensors Journal 20.1 (2019): 538-545.
  • 25. Hu, Menghan, et al. "Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation." PloS one 13.1 (2018): e0190466.
  • 26. Zhu, Kaiyin, et al. "Vision-Based Heart and Respiratory Rate Monitoring During Sleep–A Validation Study for the Population at Risk of Sleep Apnea." IEEE Journal of Translational Engineering in Health and Medicine 7 (2019): 1-8.
  • 27. Fiz, José A., et al. "Acoustic analysis of vowel emission in obstructive sleep apnea." Chest 104.4 (1993): 1093-1096.
  • 28. Fernández Pozo, Rubén, et al. "Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques." EURASIP Journal on Advances in Signal Processing 2009 (2009): 1-11.
  • 29. Espinoza-Cuadros, Fernando, et al. "Speech signal and facial image processing for obstructive sleep apnea assessment." Computational and mathematical methods in medicine 2015 (2015).
  • 30. Goldshtein, Evgenia, Ariel Tarasiuk, and Yaniv Zigel. "Automatic detection of obstructive sleep apnea using speech signals." IEEE Transactions on biomedical engineering 58.5 (2010): 1373-1382.
  • 31. Kriboy, Maya, Ariel Tarasiuk, and Yaniv Zigel. "A novel method for obstructive sleep apnea severity estimation using speech signals." 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014.
  • 32. Kriboy, Maya, Ariel Tarasiuk, and Yaniv Zigel. "Detection of Obstructive sleep apnea in awake subjects by exploiting body posture effects on the speech signal." 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE, 2014.
  • 33. Simply, Ruby M., Eliran Dafna, and Yaniv Zigel. "Obstructive sleep apnea (OSA) classification using analysis of breathing sounds during speech." 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018.

Uyku Apnesi Tespitinde Yenilikler

Year 2021, Volume: 3 Issue: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering, 32 - 39, 13.01.2021

Abstract

Yeme içme alışkanlıklarına bağlı olarak giderek artan obezite ve aşırı kilo günümüzde birçok kişinin uykuda solunum yollarının tıkanarak, horlama şeklinde kendini gösteren uyku apnesi sendromuna yakalanmasına yol açmaktadır. Klinik ve radyolojik muayene gibi teşhis araçları bulunmasına rağmen bu hastalığa teşhis koymada altın standart ölçüm yöntem, polisomnografidir. Polisomnografide; kişi hastanedeki uyku laboratuvarında bir gece misafir edilerek, EKG, EEG, EMG, EOG, ağız veya burundaki hava akımı, göğüs kafesi ve diyaframındaki genişleyip daralmalar gibi birçok fizyolojik parametrenin ölçümü ve uzman bir doktor tarafından değerlendirilmesi ile teşhis konmaktadır. Kişinin hastane ortamında uyumakta zorlanması, tetkiki yapan laboratuvarların sayısının az, tetkikin pahalı olması gibi dezavantajları sebebi ile yeni arayışlara girilmiştir. Bu çalışmada polisomnografiye alternatif olarak geliştirilen yeni yöntemlerin gözden geçirilmesi ve kıyaslanması yapılmıştır. Polisomnografiye alternatif olarak önce kişinin kendi evinde sadece birkaç parametreyi kendisinin ölçmesine dayanan yöntemler önerilmiştir. Sadece solunum sinyallerinin, EKG’nin, foto pletismografik veya pals oksimetrik sinyallerinin bir veya birkaçının kaydı ile apne tespitine dönük birçok çalışma yapılmıştır. Bu yöntemler hastanın kendi kendine kayıt yapmasındaki zorluklar sebebi ile beklenen klinik kullanım seviyesine ulaşmamıştır. Bu sorunun üstesinden gelmek üzere, kişiye elektriksel bir temas gerektirmeyen kişinin termal görüntüleme veya ultrasonik sensorlar ile takibine dayanan yöntemler önerilmiştir. Tüm gece boyunca hastanın takibini gerektiren bu yöntemlere alternatif olaraksa, kişi uyanıkken hastane ortamında alınan birkaç dakikalık konuşma sesi kayıtlarından apne tespiti konusundaki çalışmalar ortaya çıkmıştır. İncelenen makalelerin çoğundaki ortak çalışma protokolü; kaydedilen sinyallerden öz nitelik denilen apne tespitinde kullanılacak ayırt edici parametrelerin tespiti ve bunların sınıflandırıcı adı verilen yapay zeka uygulamalarına öğretilmesi ve bu uygulamaların apne tespiti yapılmak istenen kişinin verilerine göre apne var/yok veya apnenin seviyesinin tespiti şeklinde karar üretmesidir. Polisomnografiye alternatif olarak önerilen, kişinin kendi evinde uyuması sırasındaki kayıtları kullanılan yöntemler ile %90’ın, hasta uyanıkken yapılan ses kayıtlarından ise %80’in üzerinde bir başarı ile apne tespiti yapılabildiği görülmüştür. Sonuç olarak, kişi uyanıkken yapılacak birkaç dakikalık ses kaydından apnenin saptanmasının alanda büyük kolaylık sağlayacağı ancak yöntemin performansının artırılması ve kapsamlı klinik çalışmalarla doğrulanması gerektiği değerlendirildi.

References

  • 1. Kapoor, Mukesh, and Glen Greenough. "Home sleep tests for obstructive sleep apnea (OSA)." The Journal of the American Board of Family Medicine 28.4 (2015): 504-509.
  • 2. Mulgrew, Alan T., et al. "Diagnosis and initial management of obstructive sleep apnea without polysomnography: a randomized validation study." Annals of internal medicine 146.3 (2007): 157-166.
  • 3. Collop, Nancy A. "Portable monitoring for the diagnosis of obstructive sleep apnea." Current opinion in pulmonary medicine 14.6 (2008): 525-529.
  • 4. Shah, Parina, and Indira Gurubhagavatula. "Portable monitoring: practical aspects and case examples." Sleep Medicine Clinics 6.3 (2011): 355-366.
  • 5. Masa, Juan F., et al. "Effectiveness of sequential automatic-manual home respiratory polygraphy scoring." European Respiratory Journal 41.4 (2013): 879-887.
  • 6. Garg, Natasha, et al. "Home-based diagnosis of obstructive sleep apnea in an urban population." Journal of Clinical Sleep Medicine 10.8 (2014): 879-885.
  • 7. Tan, Hui-Leng, et al. "Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea." Sleep 37.2 (2014): 255-260.
  • 8. Pittman, Stephen D., et al. "Using a wrist-worn device based on peripheral arterial tonometry to diagnose obstructive sleep apnea: in-laboratory and ambulatory validation." Sleep 27.5 (2004): 923-933.
  • 9. Hedner, Jan, et al. "Sleep staging based on autonomic signals: a multi-center validation study." Journal of clinical sleep medicine (2011). 7:301– 6.
  • 10. Yalamanchali, Sreeya, et al. "Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis." JAMA Otolaryngology–Head & Neck Surgery 139.12 (2013): 1343-1350.
  • 11. Hairston, Ilana S. "The Use of WatchPAT™ for Home Sleep Testing Assessment of Sleep-Related Disordered Breathing (SDB) in Heart Disease Patients–Clinical & Operational Benefits."
  • 12. Yılmaz, Bülent, et al. "Sleep stage and obstructive apneaic epoch classification using single-lead ECG." Biomedical engineering online 9.1 (2010): 39.
  • 13. Babaeizadeh, Saeed, et al. "Automatic detection and quantification of sleep apnea using heart rate variability." Journal of electrocardiology 43.6 (2010): 535-541.
  • 14. Almazaydeh, Laiali, Khaled Elleithy, and Miad Faezipour. "Detection of obstructive sleep apnea through ECG signal features." 2012 IEEE International Conference on Electro/Information Technology. IEEE, 2012.
  • 15. Babaeizadeh, Saeed, et al. "Electrocardiogram-derived respiration in screening of sleep-disordered breathing." Journal of electrocardiology 44.6 (2011): 700-706.
  • 16. Gottlieb, Daniel J., and Naresh M. Punjabi. "Diagnosis and Management of Obstructive Sleep Apnea: A Review." Jama 323.14 (2020): 1389-1400.
  • 17. Hwang, Su Hwan, et al. "Unconstrained sleep apnea monitoring using polyvinylidene fluoride film-based sensor." IEEE Transactions on Biomedical Engineering 61.7 (2014): 2125-2134.
  • 18. Penzel, Thomas, and AbdelKebir Sabil. "The use of tracheal sounds for the diagnosis of sleep apnoea." Breathe 13.2 (2017): e37-e45.
  • 19. Kalkbrenner, Christoph, et al. "Apnea and heart rate detection from tracheal body sounds for the diagnosis of sleep-related breathing disorders." Medical & biological engineering & computing 56.4 (2018): 671-681.
  • 20. Nakano, Hiroshi, Tomokazu Furukawa, and Takeshi Tanigawa. "Tracheal sound analysis using a deep neural network to detect sleep apnea." Journal of Clinical Sleep Medicine 15.8 (2019): 1125-1133.
  • 21. Duckitt, W. D., S. K. Tuomi, and T. R. Niesler. "Automatic detection, segmentation and assessment of snoring from ambient acoustic data." Physiological measurement 27.10 (2006): 1047.
  • 22. Karunajeewa, Asela S., Udantha R. Abeyratne, and Craig Hukins. "Multi-feature snore sound analysis in obstructive sleep apnea–hypopnea syndrome." Physiological measurement 32.1 (2010): 83.
  • 23. Almazaydeh, Laiali, et al. "Apnea detection based on respiratory signal classification." Procedia Computer Science 21 (2013): 310-316.
  • 24. Baboli, Mehran, et al. "Wireless Sleep Apnea Detection Using Continuous Wave Quadrature Doppler Radar." IEEE Sensors Journal 20.1 (2019): 538-545.
  • 25. Hu, Menghan, et al. "Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation." PloS one 13.1 (2018): e0190466.
  • 26. Zhu, Kaiyin, et al. "Vision-Based Heart and Respiratory Rate Monitoring During Sleep–A Validation Study for the Population at Risk of Sleep Apnea." IEEE Journal of Translational Engineering in Health and Medicine 7 (2019): 1-8.
  • 27. Fiz, José A., et al. "Acoustic analysis of vowel emission in obstructive sleep apnea." Chest 104.4 (1993): 1093-1096.
  • 28. Fernández Pozo, Rubén, et al. "Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques." EURASIP Journal on Advances in Signal Processing 2009 (2009): 1-11.
  • 29. Espinoza-Cuadros, Fernando, et al. "Speech signal and facial image processing for obstructive sleep apnea assessment." Computational and mathematical methods in medicine 2015 (2015).
  • 30. Goldshtein, Evgenia, Ariel Tarasiuk, and Yaniv Zigel. "Automatic detection of obstructive sleep apnea using speech signals." IEEE Transactions on biomedical engineering 58.5 (2010): 1373-1382.
  • 31. Kriboy, Maya, Ariel Tarasiuk, and Yaniv Zigel. "A novel method for obstructive sleep apnea severity estimation using speech signals." 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014.
  • 32. Kriboy, Maya, Ariel Tarasiuk, and Yaniv Zigel. "Detection of Obstructive sleep apnea in awake subjects by exploiting body posture effects on the speech signal." 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE, 2014.
  • 33. Simply, Ruby M., Eliran Dafna, and Yaniv Zigel. "Obstructive sleep apnea (OSA) classification using analysis of breathing sounds during speech." 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018.
There are 33 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Metin Yıldız This is me 0000-0002-2554-6953

Publication Date January 13, 2021
Published in Issue Year 2021 Volume: 3 Issue: Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering

Cite

APA Yıldız, M. (2021). Uyku Apnesi Tespitinde Yenilikler. Natural and Applied Sciences Journal, 3(Special Issue: Full Papers of 2nd International Congress of Updates in Biomedical Engineering), 32-39.