Skip to main content

Advertisement

SpringerLink
Log in

An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

This article evaluates several indexes as support tools to diagnose patients with Sleep Apnea-Hypopnea Syndrome (SAHS). Some of these indexes, such as the Apnea-Hypopnea Index, have been standardized and studied in depth in the literature. Other indexes are used extensively in the reports that commercial polysomnographs generate. However, they have not been studied in detail and clinicians have no standardized guidelines for interpreting them. Examples are the mean and maximum duration of apneas and hypopneas. Finally, several novel indexes proposed by the authors are also evaluated. To evaluate the indexes, we have used a database of 274 patients who have undergone a polysomnographic test. Several feature selection techniques were used to assess the capability of each index to discriminate between healthy and SAHS patients. The capability of the indexes for diagnosing the patients was analyzed by using decision trees which were trained using each index individually, and all the indexes together. Our results suggest that some indexes which are often present in the reports of commercial polysomnographs provide little or no information. On the other hand, other indexes that are usually not considered have a great capability to discern between SAHS and control patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2

Similar content being viewed by others

References

  1. Almuallim, H., and T. G. Dietterich. Learning with many irrelevant features. In: Proceedings of the Ninth National Conference on Artificial Intelligence, Vol. 2, 1991, pp. 547–552.

  2. Álvarez-Estévez, D., and V. Moret-Bonillo. Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome. Expert Syst. Appl. 36(4):7778–7785, 2009.

    Article  Google Scholar 

  3. American Academy of Sleep Medicine Task Force. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22:667–689, 1999.

    Google Scholar 

  4. Bystricky, W., and A. Safer. Identification of individual sleep apnea events from the ECG using neural networks and a dynamic Markovian state model. In: Computers in Cardiology, 2004, pp. 297–308.

  5. Cabrero-Canosa, M., M. Castro-Pereiro, M. Graña-Ramos, E. Hernández-Pereira, V. Moret-Bonillo, M. Martin-Egaña, and H. Verea. An intelligent system for the detection and interpretation of sleep apneas. Expert Syst. Appl. 24:335–349, 2003.

    Article  Google Scholar 

  6. Ciftci, T. U., O. Kokturk, and S. Ozkan. Apnea-hypopnea indexes calculated using different hypopnea definitions and their relation to major symptoms. Sleep Breathing 8(3):141–146, 2004.

    Article  PubMed  Google Scholar 

  7. Durán, J., S. Esnaola, R. Rubio, and A. Izueta. Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. Am. J. Respir. Crit. Care Med. 163(3):685, 2001.

    PubMed  Google Scholar 

  8. Falabella, V., M. Lewis, and C. Campagna. Development of cardiorespiratory patterns associated with terrestrial apneas in free-ranging southern elephant seals. Physiol. Biochem. Zool. 72(1):64–70, 1999.

    Article  PubMed  CAS  Google Scholar 

  9. Fayyad, U., and K. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In: 13th International Joint Conference on Artificial Intelligence, 1993, pp. 1022–1027.

  10. Flemons, W. W. Obstructive sleep apnea. N. Engl. J. Med. 347(7):498–504, 2002.

    Article  PubMed  Google Scholar 

  11. Flemons, W. W., J. E. Remmers, and A. M. Gillis. Sleep apnea and cardiac arrhythmias. is there a relationship? Am. Rev. Respir. Dis. 148(3):618, 1993.

    Article  PubMed  CAS  Google Scholar 

  12. Ghazanshahi, S. D., and M. C. K. Khoo. Optimal ventilatory patterns in periodic breathing. Ann. Biomed. Eng. 21(5):517–530, 1993.

    Article  PubMed  CAS  Google Scholar 

  13. Hall, M. A. Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato, 1999.

  14. Hearst, M. A., S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf. Support vector machines. Intell. Syst. Appl. IEEE 13(4):18–28, 1998.

    Article  Google Scholar 

  15. Khandoker, A. H., and M. Palaniswami. Modeling respiratory movement signals during central and obstructive sleep apnea events using electrocardiogram. Ann. Biomed. Eng. 39(2):801–811, 2011.

    Article  PubMed  Google Scholar 

  16. Kira, K., and L. A. Rendell. A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning. Morgan Kaufmann Publishers Inc., 1992, pp. 249–256.

  17. Kohavi, R., and G. H. John. Wrappers for feature subset selection. Artif. Intell. 97(1–2):273–324, 1997.

    Article  Google Scholar 

  18. Laffont, F., A. Autret, M. Minz, T. Beillevaire, H. P. Cathala, and P. Castaigne. Sleep respiratory arrhythmias in control subjects, narcoleptics and non-cataplectic hypersomniacs. Electroencephalogr. Clin. Neurophysiol. 44(6):697–705, 1978.

    Article  PubMed  CAS  Google Scholar 

  19. Laxminarayan, P., S. A. Alvarez, C. Ruiz, and M. Moonis. Mining statistically significant associations for exploratory analysis of human sleep data. IEEE Trans. Inf. Technol. Biomed. 10(3):440–450, 2006.

    Article  PubMed  Google Scholar 

  20. Mantel, N. Chi-square tests with one degree of freedom; extensions of the Mantel-Haenszel procedure. J. Am. Stat. Assoc. 58(303):690–700, 1963.

    Google Scholar 

  21. Mediano, O., A. Barceló, M. de la Peña, D. Gozal, A. Agustí, and F. Barbé. Daytime sleepiness and polysomnographic variables in sleep apnoea patients. Eur. Respir. J. 30(1):110, 2007.

    Article  PubMed  CAS  Google Scholar 

  22. Morsy, A. A., and K. M. Al-Ashmouny. Sleep apnea detection using an adaptive fuzzy logic based screening system. In: 27th IEEE EMB Conference, 2005, pp. 6124–6127.

  23. Nazeran, H., A. Almas, K. Behbehani, and E. Lucas. A fuzzy inference system for detection of obstructive sleep apnea. In: 23th IEEE EMB Conference, 2001, pp. 1645–1648.

  24. Otero, A., and P. Félix. Robust Association of Pathological Respiratory Events in SAHS. Lecture Notes in Computer Science, Vol. 5518. Berlin: Springer Verlag, pp. 1020–1027, 2009.

  25. Otero, A., P. Félix, and M. R. Álvarez. Algorithms for the analysis of polysomnographic recordings with customizable criteria. Expert Syst. Appl. 38(8):10133–10146, 2011.

    Article  Google Scholar 

  26. Otero, A., P. Félix, M. R. Álvarez, and C. Zamarrón. The mean value of the descriptors of the pathological events recorded on the polysomnogram as a support tool in the diagnosis of SAHS. In: 32th Annual International IEEE, EMBS Conference, 2010, pp. 5101–5104.

  27. Otero, A., P. Félix, and J. M. Rodríguez. Database and r scripts of the paper an evaluation of indexes as support tools in the diagnosis of sleep apnea. http://biolab.uspceu.com/SAHSIndexEvaluation/, 2011. Accessed January 2012.

  28. Otero, A., P. Félix, J. M. Rodríguez, and C. Zamarrón. Evaluation of an alternative definition for the apnea-hypopnea index. In: 32th Annual International IEEE, EMBS Conference, 2010, pp. 4654–4657.

  29. Park, A. M., H. Nagase, S. V. Kumar, and Y. J. Suzuki. Effects of intermittent hypoxia on the heart. Antioxid. Redox Signal. 9(6):723–729, 2007.

    Article  PubMed  CAS  Google Scholar 

  30. Quinlan, J. R. C4. 5: Programs for Machine Learning. San Mateo: Morgan Kaufmann, 1993.

  31. Topor, Z. L., M. Pawlicki, and J. E. Remmers. A computational model of the human respiratory control system: responses to hypoxia and hypercapnia. Ann. Biomed. Eng. 32(11):1530–1545, 2004.

    Article  PubMed  Google Scholar 

  32. van Houdt, P. J., P. P. W. Ossenblok, M. G. van Erp, K. E. Schreuder, R. J. J. Krijn, P. A. J. M. Boon, and P. J. M. Cluitmans. Automatic breath-to-breath analysis of nocturnal polysomnographic recordings. Med. Biol. Eng. Comput. 49(9):1–12, 2011.

    Google Scholar 

  33. Varaday, P., T. Micsik, S. Benedeck, and Z. Benyo. A novel method for the detection of apnea and hypopnea events in respiration signals. IEEE Trans. Biomed. Eng. 33:936–942, 2002.

    Article  Google Scholar 

  34. Young, T., M. Palta, J. Dempsey, J. Skatrud, S. Weber, and S. Badr. The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328(17):1230–1235, 1993.

    Article  PubMed  CAS  Google Scholar 

  35. Young, T., P. E. Peppard, and D. J. Gottlieb. Epidemiology of obstructive sleep apnea: a population health perspective. Am. J. Respir. Crit. Care Med. 165(9):1217, 2002.

    Article  PubMed  Google Scholar 

  36. Young, T., J. Skatrud, and P. E. Peppard. Risk factors for obstructive sleep apnea in adults. JAMA 291(16):2013, 2004.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by the Spanish MEC and the European FEDER under the Grant TIN2009-14372-C03-03, by the Xunta de Galicia under the Grant INCITE08SIN002206PR and by the University San Pablo-CEU under the Grant USP BS PPC05/2010.

Author information

Authors and Affiliations

  1. Department of Information and Communications Systems Engineering, University San Pablo CEU, Boadilla del Monte, Km 5,300, Urbanización Montepríncipe, 28668, Madrid, Spain

    Abraham Otero

  2. Centro de Investigación en Tecnoloxías da Información (CITIUS), 15782, Santiago, Spain

    Paulo Félix & Jesús Presedo

  3. Division of Respiratory Medicine, University Hospital Complex of Santiago de Compostela, 15782, Santiago, Spain

    Carlos Zamarrón

Authors
  1. Abraham Otero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Félix
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jesús Presedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Zamarrón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abraham Otero.

Additional information

Associate Editor Leonidas D. Iasemidis oversaw the review of this article.

Appendix: Data used in this paper

Appendix: Data used in this paper

For the sake of reproducibility of the results, and to allow other researchers to take advantage of the data, we have made publicly available the database used in this paper. Comma separated files containing the 46,505 pathological events, and the 274 feature vectors generated from the previous file, can be found in Otero et al.27 Each of the 274 feature vectors representing patients contains the 42 indexes analyzed in this paper. In addition, each of these feature vectors also contains the patient’s age, weight, BMI, and sex. Finally, these feature vectors also have a unique patient identifier, and a diagnosis—class—that can take the values “SAHS” or “Control”. The website27 also contains scripts for the R software environment for statistical computing used in the analysis presented in this paper.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Otero, A., Félix, P., Presedo, J. et al. An Evaluation of Indexes as Support Tools in the Diagnosis of Sleep Apnea. Ann Biomed Eng 40, 1825–1834 (2012). https://doi.org/10.1007/s10439-012-0536-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-012-0536-1

Keywords

Search

Navigation