Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

Computer Methods and Programs in Biomedicine

Volumn 92, Issue 1, 2008, Pages 79-89

  Marcos, J Víctor ^a   Hornero, Roberto ^a   Álvarez, Daniel ^a   del Campo, Félix ^b   Zamarrón, Carlos ^c   López, Miguel ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b HOSPITAL UNIVERSITARIO RÍO HORTEGA   (Spain)

^c Instituto de Salud Carlos III   (Spain)

Author keywords

Multilayer perceptron neural network; Nocturnal oxygen saturation; Non linear analysis; Obstructive sleep apnoea syndrome; Pattern classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRIC LOADS; HEALTH; LIGHTNING; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NONMETALS; OXYGEN; VEGETATION;

MULTI-LAYER PERCEPTRON; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; MULTILAYER PERCEPTRON NEURAL NETWORK; NOCTURNAL OXYGEN SATURATION; NON-LINEAR ANALYSIS; NON-LINEAR FEATURES; OBSTRUCTIVE SLEEP APNOEA; OBSTRUCTIVE SLEEP APNOEA SYNDROME; OXIMETRY; OXYGEN SATURATION; PATTERN CLASSIFICATION;

NEURAL NETWORKS;

ADULT; AGED; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CENTRAL TENDENCY BIAS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; ENTROPY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; NONLINEAR SYSTEM; OXIMETRY; OXYGEN SATURATION; PERCEPTRON; SLEEP APNEA SYNDROME;

ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OXIMETRY; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SLEEP APNEA, OBSTRUCTIVE;

EID: 49949098407     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.05.006     Document Type: Article

References (49)

1. Van Houwelingen K.G., Van Uffelen R., and Van Vliet A.C.M. The sleep apnoea syndromes. European Heart Journal 20 (1999) 858-866
2. Qureshi A., and Ballard R.D. Obstructive sleep apnea. The Journal of Allergy and Clinical Immunology 112 (2003) 643-651
3. Caples S.M., Gami A.S., and Somers V.K. Obstructive sleep apnea. Annals of Internal Medicine 142 (2005) 187-197
4. Young T., Peppard P.E., and Gottlieb D.J. Epidemiology of obstructive sleep apnea: a population health perspective. American Journal of Respiratory and Critical Care Medicine 165 (2002) 1217-1239
5. Bixler E.O., Vgontzas A.N., Lin H.M., Have T.T., Rein J., Vela-Bueno A., and Kales A. Prevalence of sleep-disordered breathing in women. Effects of gender. American Journal of Respiratory and Critical Care Medicine 163 (2001) 608-613
6. Young T., Evans L., Finn L., and Palta M. Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep 20 (1997) 705-706
7. Agency for Health Care Policy and Research (AHCPR), Systematic Review of the Literature Regarding the Diagnosis of Sleep Apnea: Summary, Evidence Report/Technology Assessment: Number 1, Department of Health and Human Services, U.S. Public Health Service, 1999.
8. Bennet J.A., and Kinnear W.J.M. Sleep on the cheap: the role of overnight oximetry in the diagnosis of sleep apnoea hypopnoea syndrome. Thorax 11 (1999) 958-959
9. Gagnadoux F., Pelletier-Fleury N., Philippe C., Rakatonanahary D., and Bernard F. Home unattended vs hospital telemonitored polysomnography in suspected obstructive sleep apnea syndrome: a randomized crossover trial. Chest 121 (2002) 753-758
10. Bloch K.E. Getting the most out of nocturnal pulse oximetry. Chest 124 (2003) 1628-1630
11. Netzer N., Eliasson A.H., Netzer C., and Kristo D.A. Overnight pulse oximetry for sleep-disordered breathing in adults: a review. Chest 120 (2001) 625-633
12. Rodríguez J.M., De Lucas P., Sánchez M.J., Izquierdo J.L., Peraíta R., and Cubillo J.M. Usefulness of the visual analysis of night oximetry as a screening method in patients with suspected clinical obstructive sleep apnea syndrome. Archivos de Bronconeumología 32 (1996) 437-441
13. Vázquez J.C., Tsai W.H., Flemons W.W., Masuda A., Brant R., Hajduk E., Whitelaw W.A., and Remmers J.E. Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax 55 (2000) 302-307
14. Magalang U.J., Dmochowski J., Veeramachaneni S., Draw A., Mador M.J., El-Solh A., and Grant B.J.B. Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest 124 (2003) 1694-1701
15. Álvarez D., Hornero R., Abásolo D., Del Campo F., and Zamarrón C. Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection. Physiological Measurement 27 (2006) 399-412
16. Del Campo F., Hornero R., Zamarrón C., Abásolo D., and Álvarez D. Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea. Artificial Intelligence in Medicine 37 (2006) 111-118
17. Álvarez D., Hornero R., García M., Del Campo F., and Zamarrón C. Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure. Artificial Intelligence in Medicine 41 (2007) 13-24
18. Hornero R., Álvarez D., Abásolo D., Del Campo F., and Zamarrón C. Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome. IEEE Transactions on Biomedical Engineering 54 (2007) 107-113
19. Roche N., Herer B., Roig C., and Huchon G. Prospective testing of two models based on clinical and oximetric variables for prediction of obstructive sleep apnea. Chest 121 (2002) 747-752
20. Zhang G.P. Neural networks for classification: a survey. IEEE Transactions on Systems, Man and Cibernetics, Part C 30 (2000) 451-462
21. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford, UK
22. Baxt W.G. Application of artificial neural networks to clinical medicine. Lancet 346 (1995) 1135-1138
23. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Prentice Hall Inc., New Jersey
24. El-Solh A.A., Mador M.J., Ten-Brock E., Shucard D.W., Abul-Khoudoud M., and Grant B.J.B. Validity of neural network in sleep apnea. Sleep 22 (1999) 105-111
25. Kirby S.D., Danter W., George C.F.P., Francovic T., Ruby R.R.F., and Ferguson K.A. Neural network prediction of obstructive sleep apnea from clinical criteria. Chest 116 (1999) 409-415
26. Pincus S.M. Assessing serial irregularity and its implications for health. Annals of the New York Academy of Science 954 (2001) 245-267
27. Cohen M.E., Hudson D.L., and Deedwania P.C. Applying continuous chaotic modeling to cardiac signals. IEEE Engineering in Medicine and Biology Magazine 15 (1996) 97-102
28. Lempel A., and Ziv J. On the complexity of finite sequences. IEEE Transactions on Information Theory 22 (1976) 75-81
29. Rechtschaffen A., and Kales A. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects (1968), Brain Information Services, Brain Research Institute, University of California, Los Angeles
30. Hush D.R., and Horne B.G. Progress in supervised neural networks. IEEE Signal Processing Magazine 10 (1993) 8-39
31. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks 4 (1991) 251-257
32. Nabney I.T. NETLAB: Algorithms for Pattern Recognition (2002), Springer, Berlin
33. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 (1993) 525-533
34. Moody J.E. Note on generalization, regularization, and architecture selection in nonlinear learning systems. Proceedings of the IEEE Workshop in Neural Networks for Signal Processing (1991) 1-10
35. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (1997) 1145-1159
36. Hanley J.A., and McNeil B.J. The meaning and use of the area under a receiving operating characteristic (ROC) curve. Radiology 143 (1982) 29-36
37. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 4-37
38. Raymer M.L., Punch W.F., Goodman E.D., Kuhn L.A., and Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation 4 (2000) 164-171
39. Efron B. Bootstrap methods: another look at the jackknife. The Annals of Statistics 7 (1979) 1-26
40. Pauwels R.A., Buist A.S., Calverley P.M.A., Jenkins C.R., and Hurd S.S. Global strategy for the diagnosis management, and prevention of chronic obstructive pulmonary disease. American Journal of Respiratory and Critical care Medicine 163 (2001) 1256-1276
41. The Atlas Task Force. EEG arousals: scoring rules and examples. A preliminary report from the Sleep Disorder Task Force of the American Sleep Disorders Association. Sleep 15 (1992) 173-184
42. Marcos J.V., Hornero R., Álvarez D., Del Campo F., and López M. Applying neural network classifiers in the diagnosis of the obstructive sleep apnea syndrome from nocturnal pulse oximetric recordings. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2007) 5174-5177
43. Marcos J.V., Hornero R., Álvarez D., Del Campo F., López M., and Zamarrón C. Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. Medical and Biological Engineering and Computing 46 (2008) 323-332
44. Álvarez D., Hornero R., Marcos J.V., Del Campo F., and López M. Obstructive sleep apnea detection using clustering classification of nonlinear features from nocturnal oximetry. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2007) 1937-1940
45. Flemons W.W., Littner M.R., Rowley J.A., Gay P., McDowell W., Hudgel D.W., Douglas R., and Loube D.I. Home diagnosis of sleep apnea: a systematic review of the literature. Chest 124 (2003) 1543-1579
46. Levy P., Pepin J.L., Deschaux-Blanc C., Paramelle B., and Brambilla C. Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome. Chest 109 (1996) 395-399
47. Warley A.R., Mitchell J.H., and Stradling J.R. Evaluation of the Ohmeda 3700 pulse oximeter. Thorax 42 (1987) 892-896
48. Iber C., Ancoli-Israel S., Chesson A.L., and Quan S.F. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications (2007), American Academy of Sleep Medicine, Westchester, IL
49. Farré R., Montserrat J.M., Rotger M., Ballester E., and Navajas D. Accuracy of thermistors and thermocouples as flow-measuring devices for detecting hypopneas. European Respiratory Journal 11 (1998) 179-182