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A portable wireless device based on oximetry for sleep apnea detection

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Abstract

Obstructive sleep apnea is a highly prevalent sleep related breathing disorder and polysomnography is the gold standard exam for diagnosis. Despite providing results with high accuracy this multi-parametric test is expensive, time consuming and does not fit with the new tendency in health care that is changing the focus to prevention and wellness. Home health care is seen as a possible way to address this problematic by using minimal invasive devices, providing low cost of diagnosis and higher accessibility. To address this, a portable and automated sleep apnea detector was designed and evaluated. The device uses one SpO2 sensor and the analysis is based on the connection between oxygen saturation and apnea events. The measured signals are received in a field-programmable gate array that checks for errors and implements the communication protocols of two wireless transmitters. Two solutions were implemented for processing the data: one based on a smartphone (due to availability and low cost) and another based on a personal computer (for a higher computation capability). The algorithms were implemented in Java, for the smartphone, and in Python, for the computer. Both implementations have a graphical user interface to simplify the device operation. The algorithms were tested using a database consisting of 70 patients with the SpO2 signal collected in a Hospital. The algorithm performance achieved an average accuracy, sensitivity and specificity of 87.5, 79.5 and 90.8% respectively.

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References

  1. Ravelo-García A, Saavedra-Santana P, Juliá-Serdá G, Navarro-Mesa J, Navarro-Esteva J, Álvarez-López X, Gapelyuk A, Penzel T, Wessel N (2014) Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening. Chaos 24(2):1–8

    Article  Google Scholar 

  2. Peppard P, Young T, Barnet J, Palta M, Hagen E, Hla K (2013) Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol 177(9):1006–1014

    Article  Google Scholar 

  3. Leppänen T, Kulkas A, Duce B, Mervaala E, Töyräs J (2017) Severity of individual obstruction events is gender dependent in sleep apnea. Sleep Breath 21(2):397–404

    Article  Google Scholar 

  4. Almazaydeh L, Elleithy K, Faezipour M (2012) Detection of obstructive sleep apnea through ECG signal features. In: Proceedings of the 2012 IEEE international conference on electro information technology, Indiana, USA, 6–8 May 2012

  5. Collop N, Tracy S, Kapur V, Mehra R, Kuhlmann D, Fleishman S, Ojile J (2011) Obstructive sleep apnea devices for out-of-center (OOC) testing: technology evaluation. J Clin Sleep Med 7(5):531–548

    Google Scholar 

  6. Mendonça F, Mostafa S, Morgado-Dias F, Navarro-Mesa J, Juliá-Serdá G, Ravelo-García A (2017) A Minimally invasive portable system for sleep apnea detection. In: Proceedings of the 2017 international conference and workshop on bioinspired intelligence (IWOBI), Funchal, Portugal, 10–12 July 2017

  7. Berry R, Budhiraja R, Gottlieb D, Gozal D, Iber C, Kapur V, Marcus C, Mehra R, Parthasarathy S, Quan S, Redline S, Strohl K, Ward S, Tangredi M (2012) Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. J Clin Sleep Med 8(5):597–619

    Google Scholar 

  8. Ravelo-García A, Kraemer J, Navarro-Mesa J, Hernández-Pérez E, Navarro-Esteva J, Juliá-Serdá G, Penzel T, Wessel N (2015) Oxygen saturation and RR intervals feature selection for sleep apnea detection. Entropy 17(5):2932–2957

    Article  Google Scholar 

  9. Carter G, Coyle M, Mendelson W (2004) Validity of a portable cardio-respiratory system to collect data in the home environment in patients with obstructive sleep apnea. Sleep Hypn 6(2):74–81

    Google Scholar 

  10. Otero A, Dapena S, Félix P, Presedo J, Tarascó M (2009) A low cost screening test for obstructive sleep apnea that can be performed at the patient’s home. In: Proceedings of the IEEE international symposium on intelligent signal processing, 2009, Budapest, Hungary, 26–28 August 2009

  11. Zhao Y, Zhang H, Liu W, Ding S (2011) A snoring detector for OSAHS based on patient’s individual personality. In: Proceedings of the 2011 3rd international conference on awareness science and technology, Dalian, China, 27–30 September 2011

  12. Oliver N, Flores-Mangas F (2007) Healthgear: automatic sleep apnea detection and monitoring with a mobile phone. J Commun 2(2):1–9

    Article  Google Scholar 

  13. Jin J, Sánchez-Sinencio E (2015) A home sleep apnea screening device with time-domain signal processing and autonomous scoring capability. IEEE Trans Biomed Circuits Syst 9(1):96–104

    Article  Google Scholar 

  14. Chaudhary B, Dasti S, Park Y, Brown T, Davis H, Akhtar B (1998) Hour-to-hour variability of oxygen saturation in sleep apnea. Chest 113(3):719–722

    Article  Google Scholar 

  15. Golpe R, Jiménez A, Carpizo R, Cifrian J (1999) Utility of home oximetry as a screening test for patients with moderate to severe symptoms of obstructive sleep apnea. Sleep 22(7):932–937

    Google Scholar 

  16. Magalang U, Dmochowski J, Veeramachaneni S, Draw A, Mador M, El-Solh A, Grant B (2003) Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest 124(5):1694–1701

    Article  Google Scholar 

  17. Álvarez D, Hornero R, Abásolo D, Campo F, Zamarrón C (2006) Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection. Physiol Meas 27(4):399–412

    Article  Google Scholar 

  18. Mostafa S, Carvalho J, Morgado-Dias F, Ravelo-García A (2017) Optimization of sleep apnea detection using SpO2 and ANN. In: Proceedings of the 2017 XXVI international conference on information, communication and automation technologies (ICAT), Sarajevo, Bosnia-Herzegovina, 26–28 October 2017

  19. Kulkas A, Tiihonen P, Julkunen P, Mervaala E, Töyräs J (2013) Novel parameters indicate significant differences in severity of obstructive sleep apnea with patients having similar apnea-hypopnea index. Med Biol Eng Comput 51(6):697–708

    Article  Google Scholar 

  20. Travieso C, Alonso J, Pozo M, Ticay J, Castellanos-Dominguez G (2014) Building a cepstrum-HMM kernel for apnea identification. Neurocomputing 132:159–165

    Article  Google Scholar 

  21. Mostafa S, Mendonça F, Morgado-Dias F, Ravelo-García A (2017) SpO2 based sleep apnea detection using deep learning. In: Proceedings of the 2017 IEEE 21st international conference on intelligent engineering systems (INES), Larnaca, Cyprus, 20–23 October 2017

  22. Almazaydeh L, Faezipour M, Elleithy K (2012) A neural network system for detection of obstructive sleep apnea through SpO2 signal features. Int J Adv Comput Sci Appl 3(5):7–11

    Google Scholar 

  23. Álvarez D, Gutiérrez-Tobal G, Vaquerizo-Villar F, Barroso-García V, Crespo A, Arroyo C, Campo F, Hornero R (2016) Automated analysis of unattended portable oximetry by means of Bayesian neural networks to assist in the diagnosis of sleep apnea. In: Proceedings of the 2016 IGlobal medical engineering physics exchanges/Pan American health care exchanges (GMEPE/PAHCE), Madrid, Spain, 4–9 April 2016

  24. Garde A, Dekhordi P, Ansermino J, Dumont G, (2016) Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry. In: Proceedings of the 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), Florida, USA, 16–20 August 2016

  25. Crespo A, Álvarez D, Gutiérrez-Tobal G, Vaquerizo-Villar F, Barroso-García V, Alonso-Álvarez M, Terán-Santos J, Hornero R, Campo F (2017) Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home. Entropy 19(6):284

    Article  Google Scholar 

  26. Xie B, Minn H (2012) Real-time sleep apnea detection by classifier combination. IEEE Trans Inf Technol Biomed 16(3):469–477

    Article  Google Scholar 

  27. Al-Angari H, Sahakian A (2012) Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier. IEEE Trans Inf Technol Biomed 16(3):463–468

    Article  Google Scholar 

  28. Lázaro J, Gil E, Vergara J, Laguna P (2013) Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of pulse photoplethysmographic signal in children. IEEE J Biomed Health Inform 18(1):240–246

    Article  Google Scholar 

  29. Zhang J, Zhang Q, Wang Y, Qiu C (2013) A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment. In: Proceedings of the 2013 ACM/IEEE international conference on information processing in sensor networks, Pennsylvania, USA, 8–11 April 2013

  30. Burgos A, Gõni A, Illarramendi A, Bermúdez J (2010) Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of pulse photoplethysmographic signal in children. IEEE J Biomed Health Inform 18(1):240–246

    Google Scholar 

Download references

Acknowledgements

Acknowledgments to the Portuguese Foundation for Science and Technology for their support through Projeto Estratégico LA 9–UID/EEA/50009/2013. Acknowledgement to ARDITI–Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação under the scope of the Project M1420-09-5369-FSE-000001–PhD Studentship.

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Authors and Affiliations

  1. Universidade de Lisboa, Instituto Superior Técnico, Lisboa, Portugal

    Fábio Mendonça & Sheikh Shanawaz Mostafa

  2. Madeira Interactive Technologies Institute, Funchal, Portugal

    Fábio Mendonça, Sheikh Shanawaz Mostafa & Fernando Morgado-Dias

  3. Universidade da Madeira, Funchal, Portugal

    Fernando Morgado-Dias

  4. Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Juan L. Navarro-Mesa & Antonio G. Ravelo-García

  5. Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain

    Gabriel Juliá-Serdá

Authors
  1. Fábio Mendonça
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  2. Sheikh Shanawaz Mostafa
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  3. Fernando Morgado-Dias
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  4. Juan L. Navarro-Mesa
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  5. Gabriel Juliá-Serdá
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  6. Antonio G. Ravelo-García
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Correspondence to Fábio Mendonça.

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Mendonça, F., Mostafa, S.S., Morgado-Dias, F. et al. A portable wireless device based on oximetry for sleep apnea detection. Computing 100, 1203–1219 (2018). https://doi.org/10.1007/s00607-018-0624-7

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  • DOI: https://doi.org/10.1007/s00607-018-0624-7

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