Skip to main content

Advertisement

SpringerLink
Log in

Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The aim of this study is to assess the ability of radial basis function (RBF) classifiers as an assistant tool for the diagnosis of the obstructive sleep apnoea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS were available for our research. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. We used nonlinear features from nocturnal oxygen saturation (SaO2) to perform patients’ classification. We evaluated three different RBF construction techniques based on the following algorithms: k-means (KM), fuzzy c-means (FCM) and orthogonal least squares (OLS). A diagnostic accuracy of 86.1, 84.7 and 85.5% was provided by the networks developed with KM, FCM and OLS, respectively. The three proposed networks achieved an area under the receiver operating characteristic (ROC) curve over 0.90. Our results showed that a useful non-invasive method could be applied to diagnose OSAS from nonlinear features of SaO2 with RBF classifiers.

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Aboy M, Hornero R, Abásolo D et al (2006) Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans Biomed Eng 53:2282–2288

    Article  Google Scholar 

  2. Álvarez D, Hornero R, Abásolo D et al (2006) Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection. Physiol Meas 27:399–412

    Article  Google Scholar 

  3. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  Google Scholar 

  4. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346:1135–1138

    Article  Google Scholar 

  5. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    MATH  Google Scholar 

  6. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  7. Bjorck A (1967) Solving linear least squares problems by Gram–Schmidt orthogonalization. BIT 7:1–21

    Article  MathSciNet  Google Scholar 

  8. Bloch KE (2003) Getting the most out of nocturnal pulse oximetry. Chest 124:1628–1630

    Article  Google Scholar 

  9. Burioka N, Cornélissen G, Halberg F et al (2003) Approximate entropy of human respiratory movement during eye-closed waking and different sleep stages. Chest 123:80–86

    Article  Google Scholar 

  10. Caldirola D, Bellodi L, Caumo A et al (2004) Approximate entropy of respiratory patterns in panic disorder. Am J Psychiatr 161:79–87

    Article  Google Scholar 

  11. Catelani M, Fort A (2000) Fault diagnosis of electronic analog circuits using a radial basis function network classifier. Measurement 28:147–158

    Article  Google Scholar 

  12. Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2:302–309

    Article  Google Scholar 

  13. Cohen ME, Hudson DL, Deedwania PC (1996) Applying continuous chaotic modeling to cardiac signals. IEEE Eng Med Biol Mag 15:97–102

    Article  Google Scholar 

  14. Cohen ME, Hudson DL (2000) New chaotic methods for biomedical signal analysis. In: Proceedings of the IEEE EMBS international conference on information technology applications in biomedicine, pp 123–128

  15. El-Solh AA, Mador MJ, Ten-Brock E et al (1999) Validity of neural network in sleep apnea. Sleep 22:105–111

    Google Scholar 

  16. Er MJ, Chen W, Wu S (2005) High-speed face recognition based on discrete cosine transform and RBF neural networks. IEEE Trans Neural Netw 16:679–691

    Article  Google Scholar 

  17. Folland R, Hines EL, Boilot P et al (2002) Classifying coronary dysfunction using neural networks through cardiovascular auscultation. Med Biol Eng Comput 40:339–343

    Article  Google Scholar 

  18. Goldberger AL, Peng CK, Lipsitz LA (2002) What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 23:23–26

    Article  Google Scholar 

  19. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiving operating characteristic (ROC) curve. Radiology 143:29–36

    Google Scholar 

  20. Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same case. Radiology 148:839–843

    Google Scholar 

  21. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  22. Hornero R, Abásolo D, Aboy M et al (2005) Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension. IEEE Trans Biomed Eng 52:1671–1680

    Article  Google Scholar 

  23. Hornero R, Álvarez D, Abásolo D et al (2007) Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sep apnea syndrome. IEEE Trans Biomed Eng 54:107–113

    Article  Google Scholar 

  24. Hush DR, Horne BG (1993) Progress in supervised neural networks. IEEE Signal Process Mag 10:8–39

    Article  Google Scholar 

  25. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323

    Article  Google Scholar 

  26. Jureyda S, Shucard DW (2004) Obstructive sleep apnea: an overview of the disorder and its consequences. Semin Orthod 10:63–72

    Article  Google Scholar 

  27. Kirby SD, Danter W, George CFP et al (1999) Neural network prediction of obstructive sleep apnea from clinical criteria. Chest 116:409–415

    Article  Google Scholar 

  28. Lempel A, Ziv J (1976) On the complexity of finite sequences. IEEE Trans Inf Theory 22:75–81

    Article  MATH  MathSciNet  Google Scholar 

  29. Leonard JA, Kramer MA (1991) Radial basis function networks for classifying process faults. IEEE Control Syst Mag 11:31–38

    Article  Google Scholar 

  30. Magalang UJ, Dmochowski J, Veeramachaneni S et al (2003) Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest 124:1694–1701

    Article  Google Scholar 

  31. Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  32. Netzer N, Eliasson AH, Netzer C et al (2001) Overnight pulse oximetry for sleep-disordered-breathing in adults: a review. Chest 120:625–633

    Article  Google Scholar 

  33. Pincus SM (2001) Assessing serial irregularity and its implications for health. Ann N Y Acad Sci 954:245–267

    Article  Google Scholar 

  34. Rechtschaffen A, Kales A (1968) A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Brain information services, Brain research institute, University of California, Los Angeles

    Google Scholar 

  35. Roche N, Herer B, Roig C et al (2002) Prospective testing of two models based on clinical and oximetric variables for prediction of obstructive sleep apnea. Chest 121:747–752

    Article  Google Scholar 

  36. Schlosshan D, Elliot MW (2004) Sleep 3: clinical presentation and diagnosis of the obstructive sleep apnoea hypopnoea syndrome. Thorax 59:347–352

    Article  Google Scholar 

  37. The Atlas Task Force (1992) EEG arousals: scoring rules and examples. A preliminary report from the Sleep Disorder Task Force of the American Sleep Disorders Association. Sleep 15:173–184

    Google Scholar 

  38. Van Houwelingen KG, Van Uffelen R, Van Vliet ACM (1999) The sleep apnoea syndromes. Eur Heart J 20:858–866

    Article  Google Scholar 

  39. Vazquez JC, Tsai WH, Flemons WW et al (2000) Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax 55:302–307

    Article  Google Scholar 

  40. Young T, Peppard PE, Gottlieb DJ (2002) Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med 165:1217–1239

    Article  Google Scholar 

  41. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 30:451–462

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by Consejería de Sanidad de la Junta de Castilla y León under project SAN/191/VA03/06. The authors are also thankful for the fruitful comments of the referees.

Author information

Authors and Affiliations

  1. Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Camino del cementerio, s/n, 47011, Valladolid, Spain

    J. Víctor Marcos, Roberto Hornero, Daniel Álvarez & Miguel López

  2. Servicio de Neumología, Hospital del Río Hortega, Valladolid, Spain

    Félix del Campo

  3. Servicio de Neumología, Hospital Clínico Universitario, Santiago de Compostela, Spain

    Carlos Zamarrón

Authors
  1. J. Víctor Marcos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Hornero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Álvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Félix del Campo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miguel López
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Carlos Zamarrón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Víctor Marcos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Marcos, J.V., Hornero, R., Álvarez, D. et al. Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Med Biol Eng Comput 46, 323–332 (2008). https://doi.org/10.1007/s11517-007-0280-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-007-0280-0

Keywords

Search

Navigation