Artificial Evolution Methods in the Biological and Biomedical SciencesView this Special Issue
Research Article | Open Access
Evolutionary Selection of Features for Neural Sleep/Wake Discrimination
In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on a microcontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often require more computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications, where computational resources and energy are limited, this is especially detrimental. Neuroevolutionary methods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of the mobile system.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagation of errors,” Nature, vol. 323, pp. 533–536, 1986.
- D. Floreano, P. Dürr, and C. Mattiussi, “Neuroevolution: from architectures to learning,” Evolutionary Intelligence, vol. 1, no. 1, pp. 47–62, 2008.
- 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.
- 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.
- A. Sadeh and C. Acebo, “The role of actigraphy in sleep medicine,” Sleep Medicine Reviews, vol. 6, no. 2, pp. 113–124, 2002.
- 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.
- 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.
- 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.
- R. D. Ogilvie, “The process of falling asleep,” Sleep Medicine Reviews, vol. 5, no. 3, pp. 247–270, 2001.
- 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.
- 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.
- 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.
- 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.
- X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999.
- K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,” Evolutionary Computation, vol. 10, no. 2, pp. 99–127, 2002.
- 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.
- F. Gruau, “Automatic definition of modular neural networks,” Adaptive Behavior, vol. 3, no. 2, pp. 151–183, 1994.
- J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, Mass, USA, 1994.
- 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.
- 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.
- T. Reil, “Artificial genomes as models of gene regulation,” in On Growth, Form and Computers, pp. 256–277, Academic Press, London, UK, 2003.
- C. Mattiussi, D. Marbach, P. Dürr, and D. Floreano, “The age of analog networks,” AI Magazine, vol. 29, no. 3, pp. 63–76, 2008.
- 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.
- 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.
- 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.
- 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.
- G. Gusfield, Algorithms on Strings, Trees, and Sequences, Cambridge University Press, Cambridge, UK, 1997.
- C. Mattiussi, Evolutionary synthesis of analog networks, Ph.D. dissertation, EPFL, Lausanne, Switzerland, 2005.
- A. Wagner, “Robustness, evolvability, and neutrality,” FEBS Letters, vol. 579, no. 8, pp. 1772–1778, 2005.
- 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.
- 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.
- 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.
- 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.
- R. McGill, J. W. Tukey, and W. A. Larsen, “Variations of box plots,” The American Statistician, vol. 32, no. 1, pp. 12–16, 1978.
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