Sleep Versus Wake Classification From Heart Rate Variability Using Computational Intelligence: Consideration of Rejection in Classification Models

IEEE Transactions on Biomedical Engineering

Volumn 55, Issue 1, 2008, Pages 108-118

  Lewicke, Aaron ^a   Sazonov, Edward ^a   Corwin, Michael J ^b,c   Neuman, Michael ^d   Schuckers, Stephanie ^a  

^a Clarkson University   (United States)

^b BOSTON MEDICAL CENTER   (United States)

^c BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d Michigan Technological University   (United States)

Author keywords

Heart rate variability; infants; reliability; sleep wake classification

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ELECTROCARDIOGRAPHY; EVALUATION; HEART RATE; HUMAN; METHODOLOGY; NEWBORN; PHYSIOLOGY; POLYSOMNOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SLEEP STAGE; WAKEFULNESS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROCARDIOGRAPHY; HEART RATE; HUMANS; INFANT, NEWBORN; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SLEEP STAGES; WAKEFULNESS;

EID: 39649120818     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2007.900558     Document Type: Article

References (39)

1. R. Agarwal and J. Gotman, “Computer-assisted sleep staging,” IEEE Trans. Biomed. Eng., vol. 48, no. 12, pp. 1412–1423, Dec. 2001.
2. N. Ampazis and S. Perantonis, “Two highly efficient second-order algorithms for training feedforward networks,” IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1064–1074, Sep. 2002.
3. D. Barnett, A. Laposky, C. Thomas, and M. Anch, “Neural network scoring of rat sleep stages,” in Proc. IEEE 1st BMES/EMBS Conf., Oct. 1999, p. 389.
4. C. K. Chow, “On optimum recognition error and reject tradeoff,” IEEE Trans. Inf. Theory, vol. IT-16, no. 1, pp. 41–46, Jan. 1970.
5. R. J. Cole, D. F. Kripke, W. Gruen, D. J. Mullaney, and J. C. Gillin, “Automatic sleep/wake identification from wrist actigraphy,” Sleep, vol. 15, pp. 461–469, 1992.
6. N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines And Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, Mar. 2000.
7. D. H. Crowell, T. T. Hoppenbrouwers, C. E. Hunt, L. E. Kapuniai, G. Lister, M. R. Neuman, M. Peucker, S. L. Ward, D. E. Weese-Mayer, and M. Willinger, “Infant polysomnograph: Reliability. Collaborative home infant monitoring evaluation (chime) steering committee,” Sleep, vol. 20, no. 7, pp. 553–560, 1997.
8. C. De Stefano, C. Sansone, and M. Vento, “To reject or not to reject: That is the question—An answer in case of neural classifiers,” IEEE Trans. Syst. Man Cybern. C, Appl. Rev., vol. 30, no. 1, pp. 84–94, Feb. 2000.
9. R. Duda, P. Hart, and D. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
10. P. Estevez, C. Held, C. Holzmann, C. Perez, J. Perez, J. Heiss, M. Garrido, and P. Peirano, “Polysomnographic pattern recognition for automated classification of sleep-waking states in infants,” Med. Bio. Eng. Comput., vol. 40, pp. 105–113, 2002.
11. J. L. Fleiss, Statistical Methods for Rates and Proportions. New York: Wiley, 1973.
12. G. G. Haddad, H. J. Jeng, T. L. Lai, and R. B. Mellins, “Determination of sleep state in infants using respiratory variability,” Pediatric Res., vol. 21, no. 6, pp. 556–562, 1987.
13. R. Harper,V. Schechtman, and K. Kluge, “Machine classification of infant sleep state using cardiorespiratory measures,” Electroencephalogr. Clin. Neurophysiol., vol. 67, pp. 379–387, 1987.
14. R. Harper,V. Schechtman, and K. Kluge, “Machine classification of infant sleep state using cardiorespiratory measures,” Electroencephalogr. Clin. Neurophysiol., vol. 67, pp. 379–387, 1987.
15. J. Heiss, C. Held, P. Estevez, C. Perez, C. Holzmann, and J. Perez, “Classification of sleep stages in infants: A neuro fuzzy approach,” IEEE Eng. Med. Biol. Mag., vol. 21, no. 5, pp. 147–151, Sep./Oct. 2002.
16. M. E. Hellman, “The nearest neighbor classification rule with a reject option,” IEEE Trans. Syst. Sci. Cybern., vol. 6, no. 3, pp. 179–185, Jul. 1970.
17. C. Holzmann, C. Perez, C. Held, M. S. Martin, F. Pizarro, J. Perez, M. Garrido, and P. Peirano, “Expert-system classification of sleep/waking states in infants,” Med. Biol. Eng. Comput., vol. 37, pp. 466–476, 1999.
18. T. Hoppenbrouwers, M. Neuman, M. Corwin, J. Silvestri, T. Baird, D. Crowell, C. Hunt, M. Sackner, G. Lister, M. Willinger, and The CHIME Study Group, “Multivariable cardiorespiratory monitoring at home: Collaborative home infant monitoring evaluation (chime),” in Proc. IEEE Annu. Int. Conf. Eng. Med. Biol., 1996, pp. 61–62.
19. B. Kim and K. Park, “Automatic sleep stage scoring system using genetic algorithms and neural network,” in Proc. 22nd IEEE Eng. Med. Biol. Soc. (EMBS) Conf., Jul. 2000, pp. 849–850.
20. T. Kohonen, “Self-organizing maps,” in Springer Series in Information Sciences, 3rd ed. New York: Springer-Verlag, 2001, vol. 30.
21. T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, and K. Torkkola, LVQ PAK: The Learning Vector Quantization Program Package, Helsinki University of Technology, Helsinki, Finland, 1995.
22. I. Koprinska, G. Pfurtscheller, and D. Flotzinger, “Sleep classification in infants by decision tree-based neural networks,” Artif. Intell. Med., vol. 8, pp. 387–401, 1996.
23. A. T. Lewicke, E. S. Sazonov, M. J. Corwin, and S. A. C. Schuckers, “Reliable determination of sleep versuswake from heart rate variability using neural networks,” in Proc. Int. Joint Conf. Neural Netw., Jul. 2005, pp. 2394–2399.
24. A. T. Lewicke, E. S. Sazonov, and S. A. C. Schuckers, “Sleep-wake identification in infants: Heart rate variability compared to actigraphy,” in Proc. 26th IEEE Eng. Med. Biol. Soc. (EMBS) Conf., Sep. 2004, pp. 442–445.
25. M. Lisenby, P. Richardson, and A. Welch, “Detection of cyclic sleep phenomena using instantaneous heart rate,” Electroencephalogr. Clin. Neurophysiol., vol. 40, pp. 169–177, 1976.
26. A. Madevska-Bogdanova and D. Nikolic, “A new approach of modifying SVM outputs,” in Proc. Int. Joint Conf. Neural Netw., Jul. 2000, pp. 395–398.
27. D. Meyer, “Support vector machines: The interface to LINSVM in package e1071,” R-News, vol. 1, no. 3, pp. 23–26, 2001.
28. G. P. Nason, T. Sapatinas, and A. Sawczenko, “Wavelet packet modelling of infant sleep state using heart rate data,” Sankhya: The Indian J. Statist., vol. 63, pp. 199–217, 2001.
29. M. R. Neuman, H. Watson, R. S. Mendenhall, J. T. Zoldak, J. M. Di Fiore, M. Peucker, T.M. Baird, D. H. Crowell, T. T. Hoppenbrouwers, D. Hufford, C. E. Hunt, M. J. Corwin, L. R. Tinsley, D. E. Weese-Mayer, M. A. Sackner, and The CHIME Study Group, “Cardiopulmonary monitoring at home: The chime monitor,” Physiol. Meas., vol. 22, no. 2, pp. 267–286, 2001.
30. L. Quanzheng and G. Xiaorong, “Subsection approximate entropy and its application in sleep staging,” in Proc. IEEE 1st BMES/EMBS Conf., Oct. 1999, p. 900.
31. E. Sazonov, N. Sazonova, S. A. C. Schuckers, and M. Neuman, “Activity-based sleep-wake identification in infants,” Physiol. Meas., vol. 25, no. 5, pp. 1291–1304, Oct. 2004.
32. M. Scher, S. Dokianakis, D. Steppe, D. Banks, and R. Sclabassi, “Computer classification of state in healthy preterm neonates,” Sleep, vol. 20, no. 2, pp. 132–141, 1997.
33. M. Scher, S. Dokianakis, M. Sun, D. Steppe, R. Guthrie, and R. Sclabassi, “Computer classification of sleep in preterm and full-term neonates at similar postconceptional term ages,” Sleep, vol. 19, no. 1, pp. 18–25, 1996.
34. M. Scher, M. Sun, G. Hatzilabrou, N. Greenberg, G. Cebulka, D. Krieger, R. Guthrie, and R. Sclabassi, “Computer analyses of EEG-sleep in the neonate: Methodological considerations,” J. Clin. Neruophysiol., vol. 7, no. 3, pp. 417–441, 1990.
35. S. Stemler, “An overview of content analysis,” Practical Assess. Res. Eval., vol. 7, 2001, ISSN 1531-7714.
36. S. Stemler, “A comparison of consensus, consistency, and measurement approaches to estimating interrater reliability,” Practical Assess. Res. Eval., vol. 9, 2004, ISSN 1531-7714.
37. A. Welch and P. Richardson, “Computer sleep stage classification using heart rate data,” Electroencephalogr. Clin. Neurophysiol., vol. 34, pp. 145–152, 1973.
38. X. Xu and S. Schuckers, “Automatic detection of artifacts in heart period data,” J. Electrocardiol., vol. 33, 2001.
39. J. Yen and R. Langari, Fuzzy Logic: Intelligence, Control, and Information, 1st ed. Englewood Cliffs, NJ: Prentice-Hall, 1998.