Table of Contents
Journal of Artificial Evolution and Applications
Volume 2009 (2009), Article ID 179680, 9 pages
http://dx.doi.org/10.1155/2009/179680
Research Article

Evolutionary Selection of Features for Neural Sleep/Wake Discrimination

Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

Received 15 November 2008; Accepted 19 February 2009

Academic Editor: Janet Clegg

Copyright © 2009 Peter Dürr et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagation of errors,” Nature, vol. 323, pp. 533–536, 1986. View at Publisher · View at Google Scholar
  2. D. Floreano, P. Dürr, and C. Mattiussi, “Neuroevolution: from architectures to learning,” Evolutionary Intelligence, vol. 1, no. 1, pp. 47–62, 2008. View at Publisher · View at Google Scholar
  3. C. Mattiussi and D. Floreano, “Analog genetic encoding for the evolution of circuits and networks,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 5, pp. 596–607, 2007. View at Publisher · View at Google Scholar
  4. A. Rechtschaffen, A. Kales, R. Berger, and W. Dement, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, Public Health Service, U.S. Government Printing Office, Washington, DC, USA, 1968.
  5. A. Sadeh and C. Acebo, “The role of actigraphy in sleep medicine,” Sleep Medicine Reviews, vol. 6, no. 2, pp. 113–124, 2002. View at Publisher · View at Google Scholar
  6. C. P. Pollak, W. W. Tryon, H. Nagaraja, and R. Dzwonczyk, “How accurately does wrist actigraphy identify the states of sleep and wakefulness?,” Sleep, vol. 24, no. 8, pp. 957–965, 2001. View at Google Scholar
  7. W. Karlen, C. Mattiussi, and D. Floreano, “Adaptive sleep/wake classification based on cardiorespiratory signals for wearable devices,” in Proceedings of the IEEE on Biomedical Circuits and Systems Conference (BIOCAS '07), pp. 203–206, Montreal, Canada, November 2007. View at Publisher · View at Google Scholar
  8. W. Karlen, C. Mattiussi, and D. Floreano, “Sleep and wake classification with ECG and respiratory effort signals,” to appear in IEEE Transactions on Biomedical Circuits and Systems.
  9. R. D. Ogilvie, “The process of falling asleep,” Sleep Medicine Reviews, vol. 5, no. 3, pp. 247–270, 2001. View at Publisher · View at Google Scholar · View at PubMed
  10. G. P. Zhang, “Neural networks for classification: a survey,” IEEE Transactions on Systems, Man and Cybernetics, Part C, vol. 30, no. 4, pp. 451–462, 2000. View at Publisher · View at Google Scholar
  11. M. Rocha, P. Cortez, and J. Neves, “Evolution of neural networks for classification and regression,” Neurocomputing, vol. 70, no. 16–18, pp. 2809–2816, 2007. View at Publisher · View at Google Scholar
  12. M. Čepek, M. Šnorek, and V. Chudáček, “ECG signal classification using GAME neural network and its comparison to other classifiers,” in Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN '08), vol. 5163 of Lecture Notes in Computer Science, pp. 768–777, Prague, Czech Republic, September 2008. View at Publisher · View at Google Scholar
  13. L. Chen and D. Alahakoon, “NeuroEvolution of augmenting topologies with learning for data classification,” in Proceedings of the International Conference on Information and Automation (ICIA '06), pp. 367–371, Shandong, China, December 2006. View at Publisher · View at Google Scholar
  14. X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999. View at Publisher · View at Google Scholar
  15. K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evolutionary Computation, vol. 10, no. 2, pp. 99–127, 2002. View at Publisher · View at Google Scholar · View at PubMed
  16. R. S. Zebulum, M. Vellasco, and M. A. Pacheco, “Variable length representation in evolutionary electronics,” Evolutionary Computation, vol. 8, no. 1, pp. 93–120, 2000. View at Publisher · View at Google Scholar · View at PubMed
  17. F. Gruau, “Automatic definition of modular neural networks,” Adaptive Behavior, vol. 3, no. 2, pp. 151–183, 1994. View at Publisher · View at Google Scholar
  18. J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, Mass, USA, 1994.
  19. J. Bongard, “Evolving modular genetic regulatory networks,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), vol. 2, pp. 1872–1877, Honolulu, Hawaii, USA, May 2002.
  20. T. Reil, “Dynamics of gene expression in an artificial genome-implications for biological and artificial ontogeny,” in Proceedings of the 5th European Conference on Artificial Life (ECAL '99), pp. 457–466, Lausanne, Switzerland, September 1999.
  21. T. Reil, “Artificial genomes as models of gene regulation,” in On Growth, Form and Computers, pp. 256–277, Academic Press, London, UK, 2003. View at Google Scholar
  22. C. Mattiussi, D. Marbach, P. Dürr, and D. Floreano, “The age of analog networks,” AI Magazine, vol. 29, no. 3, pp. 63–76, 2008. View at Google Scholar
  23. J. Reisinger and R. Miikkulainen, “Acquiring evolvability through adaptive representations,” in Proceedings of the 9th Annual Genetic and Evolutionary Computation Conference (GECCO '07), pp. 1045–1052, ACM Press, London, UK, July 2007. View at Publisher · View at Google Scholar
  24. P. Dürr, C. Mattiussi, and D. Floreano, “Neuroevolution with analog genetic encoding,” in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN '06), vol. 9, pp. 671–680, Springer, Reykjavik, Iceland, September 2006.
  25. A. Soltoggio, P. Dürr, C. Mattiussi, and D. Floreano, “Evolving neuromodulatory topologies for reinforcement learning-like problems,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), P. Angeline, M. Michaelewicz, G. Schonauer, X. Yao, and Z. Zalzala, Eds., pp. 2471–2478, IEEE Press, Singapore, September 2007. View at Publisher · View at Google Scholar
  26. P. Dürr, C. Mattiussi, A. Soltoggio, and D. Floreano, “Evolvability of neuromodulated learning for robots,” in Proceedings of the ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS '08), pp. 41–46, Edinburgh, Scotland, August 2008. View at Publisher · View at Google Scholar
  27. G. Gusfield, Algorithms on Strings, Trees, and Sequences, Cambridge University Press, Cambridge, UK, 1997.
  28. C. Mattiussi, Evolutionary synthesis of analog networks, Ph.D. dissertation, EPFL, Lausanne, Switzerland, 2005.
  29. A. Wagner, “Robustness, evolvability, and neutrality,” FEBS Letters, vol. 579, no. 8, pp. 1772–1778, 2005. View at Publisher · View at Google Scholar · View at PubMed
  30. J. Principe and A. Tome, “Performance and training strategies in feedforward neural networks: an application to sleep scoring,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '89), vol. 1, pp. 341–346, Washington, DC, USA, June 1989.
  31. D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in Proceedings of International Joint Conference on Neural Networks (IJCNN '90), pp. 21–26, San Diego, Calif, USA, June 1990.
  32. M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 989–993, 1994. View at Publisher · View at Google Scholar · View at PubMed
  33. C. Mattiussi, P. Dürr, and D. Floreano, “Center of mass encoding: a self-adaptive representation with adjustable redundancy for real-valued parameters,” in Proceedings of the 9th Annual Genetic and Evolutionary Computation Conference (GECCO '07), pp. 1304–1311, London, UK, July 2007. View at Publisher · View at Google Scholar
  34. R. McGill, J. W. Tukey, and W. A. Larsen, “Variations of box plots,” The American Statistician, vol. 32, no. 1, pp. 12–16, 1978. View at Publisher · View at Google Scholar