Advertisement

Soft Computing

, Volume 17, Issue 10, pp 1929–1937 | Cite as

Classification of signals by means of Genetic Programming

  • Enrique Fernández-Blanco
  • Daniel Rivero
  • Marcos Gestal
  • Julián Dorado
Methodologies and Application

Abstract

This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.

Keywords

Genetic Programming Automatic feature extraction Automatic classification Signal processing 

Notes

Acknowledgments

First of all, the authors want to thank the support from the CESGA to execute the test of this paper. The authors wants also to thank the support from different institutions who has funded this work, in particularly, projects: RD07/0067/0005 funded by the Carlos III Health and 10SIN105004PR funded by Economy and Industry Department of Xunta de Galicia.

References

  1. Abarbanel HDI, Brown R, Kennel MB (1991) Lyapunov exponents in chaotic systems: their importance and their evaluation using observed data. Int J Mod Phys 5(9):1347–1375. doi: 10.1142/S021797929100064X CrossRefMATHGoogle Scholar
  2. Addison PS (2002) The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. Institute of Physics Publishing, BristolCrossRefGoogle Scholar
  3. Ahsan MR, Ibrahimy MI, Khalifa OO (2009) EMG signal classification for human computer interaction: a review. Eur J Sci Res 33(3):480–501Google Scholar
  4. Anderson CW, Stolz EA, Shamsunder S (1998) Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Trans Biomed Eng 45(3):277–286. doi: 10.1109/10.661153 CrossRefGoogle Scholar
  5. Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E Stat Nonlin Soft Matter Phys 64 (6) doi: 061907
  6. Bazi Y, Melgani F (2006) Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 44(11):3374–3385. doi: 10.1109/tgrs.2006.880628 CrossRefGoogle Scholar
  7. Buteneers P, Verstraeten D, van Mierlo P, Wyckhuys T, Stroobandt D, Raedt R, Hallez H, Schrauwen B (2011) Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing. Artif Intell Med 53(3):215–223. doi: 10.1016/j.artmed.2011.08.006 CrossRefGoogle Scholar
  8. Cardoso JF (1998) Blind signal separation: statistical principles. Proc IEEE 86(10):2009–2025. doi: 10.1109/5.720250 CrossRefGoogle Scholar
  9. Dalponte M, Bovolo F, Bruzzone L (2007) Automatic selection of frequency and time intervals for classification of EEG signals. Electron Lett 43(25):1406–1408. doi: 10.1049/el:20072428 CrossRefGoogle Scholar
  10. Deriche M, Al-ani A (2001) A new algorithm for EEG feature selection using mutual information. In: IEEE International Conference of the Acoustics Speech and Signal Processing 2001, pp 1057–1060. doi: 10.1109/ICASSP.2001.941101
  11. Dolinsky JU, Jenkinson ID, Colquhoun GJ (2007) Application of Genetic Programming to the calibration of industrial robots. Comput Ind 58(3):255–264. doi: 10.1016/j.compind.2006.06.003 CrossRefGoogle Scholar
  12. Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. Systems, man, and cybernetics, Part C: applications and reviews. IEEE Transactions on 40 (2):121–144. doi: 10.1109/TSMCC.2009.2033566 Google Scholar
  13. Guler I, Ubeyli ED (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 148(2):113–121. doi: 10.1016/j.jneumeth.2005.04.01 CrossRefGoogle Scholar
  14. Guler NF, Ubeylib I, Guler ED, Guler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29:506–514. doi: 10.1016/j.eswa.2005.04.011 CrossRefGoogle Scholar
  15. Guo L, Rivero D, Seoane JA, Pazos A Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the first ACM/SIGEVO Summit on genetic and evolutionary computation, Shanghai, China, 2009. pp 177–184. doi: 10.1145/1543834.1543860
  16. Hong G, Jack LB, Nandi AK (2005) Feature generation using genetic programming with application to fault classification. In: IEEE Transactions on Systems, Man and Cybernetics, Part B: cybernetics 35 (1):89–99Google Scholar
  17. Hsu WY, Lin CH, Hsu HJ, Chen PH, Chen IR (2012) Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data. Expert Syst Appl 39(3):2743–2749. doi: 10.1016/j.eswa.2011.08.132 CrossRefMathSciNetGoogle Scholar
  18. Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4–5):411–430. doi: 10.1016/s0893-6080(00)00026-5 CrossRefGoogle Scholar
  19. Kannathal N, Acharya UR, Lim CM, Sadasivan PK (2005a) Characterization of EEG—a comparative study. Comput Methods Prog Biomed 80(1):17–23. doi: 10.1016/j.cmpb.2005.06.005 CrossRefGoogle Scholar
  20. Kannathal N, Choob ML, Acharyab UR, Sadasivana PK (2005b) Entropies for the detection of epilepsy in EEG. Comput Methods Programs Biomed 80(3):187–194. doi: 10.1016/j.cmpb.2005.06.01 CrossRefGoogle Scholar
  21. Kishore JK, Patnaik LM, Mani V, Agrawal VK (2000) Application of genetic programming for multi category pattern classification. IEEE Trans Evol Comput 4(3):242–258. doi: 10.1109/4235.873235 CrossRefGoogle Scholar
  22. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. The MIT Press, CambridgeMATHGoogle Scholar
  23. Lima CAM, Coelho ALV (2011) Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study. Artif Intell Med 53(2):83–95. doi: 10.1016/j.artmed.2011.07.003 CrossRefGoogle Scholar
  24. Lopes R, Betrouni N (2009) Fractal and multifractal analysis: a review. Med Image Anal 13(4):634–649. doi: 10.1016/j.media.2009.05.003 CrossRefGoogle Scholar
  25. Mallat S, Hwang WL (1992) Singularity detection and processing with wavelets. IEEE Trans Infor Theory 38(2):617–643. doi: 10.1109/18.119727 MathSciNetCrossRefMATHGoogle Scholar
  26. Mohseni HR, Maghsoudi A, Shamsollahi B Seizure Detection in EEG signals: a comparison of different approaches. In: Conference of the IEEE Engineering in Medicine and Biology Society 2006, pp 6724–6727. doi: 10.1109/IEMBS.2006.260931
  27. Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3(2):199–230. doi: 10.1162/evco.1995.3.2.199 CrossRefGoogle Scholar
  28. Nigam VP, Graupe D (2004) A neural-network-based detection of epilepsy. Neurol Res 26(1):55–60. doi: 10.1179/016164104773026534 CrossRefGoogle Scholar
  29. Polat K, Günes S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 32(2):625–631. doi: 10.1016/j.amc.2006.09.022 Google Scholar
  30. Rabuñal JR, Puertas J, Suarez J, Rivero D (2007) Determination of the unit hydrograph of a typical urban basin using Genetic Programming and artificial neural networks. Hydrol Process 21:476–485. doi: 10.1002/hyp.6250 CrossRefGoogle Scholar
  31. Rivero D, Rabuñal JR, Dorado J, Pazos A (2005) Time series forecast with anticipation using Genetic Programming. Lect Notes Comput Sci 3512:968–975. doi: 10.1007/11494669_119 CrossRefGoogle Scholar
  32. Rivero D, Dorado J, Rabuñal J, Pazos A (2009) Evolving simple feed-forward and recurrent ANN’s for signal classification: A comparison. IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp 2685–2692.doi: 10.1109/IJCNN.2009.5178621
  33. Rivero D, Fernandez-Blanco E, Dorado J, Pazos A (2011a) A new signal classification technique by means of Genetic Algorithms and kNN. IEEE Congress on Evolutionary Computation (CEC), pp 581–586. doi: 10.1109/CEC.2011.5949671
  34. Rivero D, Fernandez-Blanco E, Dorado J, Pazos A (2011b) Using recurrent ANNs for the detection of epileptic seizures in EEG signals. IEEE Congress on Evolutionary Computation (CEC), pp 587–592. doi: 10.1109/CEC.2011.5949672
  35. Rosenblum MG, Pikovsky AS, Kurths J (1996) Phase synchronization of chaotic oscillators. Phys Rev Lett 76(11):1804–1807. doi: 10.1103/PhysRevLett.76.1804 CrossRefGoogle Scholar
  36. Sadati N, Mohseni HR, Maghsoudi A (2006) Epileptic Seizure Detection using neural fuzzy networks. In: IEEE International Conference on Fuzzy Systems, pp 596–600 doi: 10.1109/FUZZY.2006.1681772
  37. Schneider M, Mustaro PN Lima CAM (2009) Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal. In: Proceedings of the 2009 international joint conference on Neural Networks, pp 2841–2845. doi: 10.1109/IJCNN.2009.5179059
  38. Schröder M, Bogdan M, Rosenstiel W, Hinterberger T, Birbaumer N (2003) Automated EEG feature selection for brain computer interfaces. In: Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, Capri Island, Italy, pp 626–629. doi: 10.1109/CNE.2003.1196906
  39. Srinivasan V, Eswaran C, Sriraam N (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 29(6):647–660. doi: 10.1007/s10916-005-6133-1 CrossRefGoogle Scholar
  40. Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093. doi: 10.1016/j.eswa.2006.02.005 CrossRefGoogle Scholar
  41. Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8659–8666. doi: 10.1016/j.eswa.2010.06.065 CrossRefGoogle Scholar
  42. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79(1):61–78. doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 CrossRefGoogle Scholar
  43. Tzallas AT, Tsipouras MG, Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 7(3):1–13. doi: 10.1155/2007/80510 CrossRefGoogle Scholar
  44. Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Infor Technol Biomed 13(5):703–710. doi: 10.1109/TITB.2009.2017939 CrossRefGoogle Scholar
  45. Übeyli ED (2009) Lyapunov exponents/probabilistic neural networks for analysis of EEG signals. Expert Syst Appl 37(2):985–992. doi: 10.1016/j.eswa.2009.05.078 CrossRefGoogle Scholar
  46. Zhan YQ, Shen DG (2006) Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans Med Imaging 25(3):256–272. doi: 10.1109/tmi.2005.862744 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Enrique Fernández-Blanco
    • 1
  • Daniel Rivero
    • 1
  • Marcos Gestal
    • 1
  • Julián Dorado
    • 1
  1. 1.University of A Coruña, Faculty of Computer ScienceA CoruñaSpain

Personalised recommendations