Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

The classification of signals is usually based on the extraction of various features that subsequently will be used as an input to a classifier. These features are extracted as a result of the experts’ prior knowledge, which may often involve a lack of the information necessary for an accurate classification in all cases. This study proposes a new technique, in which a genetic algorithm is used to automatically extract frequency-domain features from a set of signals, with no need of prior knowledge. This allows, first, to achieve greater accuracy in the classification of signals, and, secondly, to discover new data on the signals to be classified. This system was used to solve a well-known problem: classification of electroencephalogram (EEG) signals, and its results show a better performance in comparison with other works on the same problem.

Inspec keywords: feature extraction; pattern classification; learning (artificial intelligence); expert systems; genetic algorithms; signal classification; frequency-domain analysis

Other keywords: EEG signals; genetic algorithms; automatic frequency band selection; experts prior knowledge; k-nearest neighbour; electroencephalogram signals; frequency-domain features; feature extraction; signal classification

Subjects: Mathematical analysis; Digital signal processing; Knowledge engineering techniques; Optimisation techniques; Signal processing and detection; Data handling techniques; Optimisation techniques; Mathematical analysis

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • J.H. Holland . (1975) Adaptation in natural and artificial systems.
    5. 5)
    6. 6)
      • Mishra, A.K., Feng, H., Mulgrew, B.: `Fractal feature based radar signal classification', IET Int. Conf. on Radar Systems, October 2007, p. 1–4.
    7. 7)
      • Deriche, M., Al-ani, A.: `A new algorithm for EEG feature selection using mutual information', 2001 IEEE Int. Conf. Proc. Acoustics, Speech, and Signal Processing, 2001.
    8. 8)
      • Schneider, M., Mustaro, P.N., Lima, C.A.M.: `Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal', Proc. 2009 Int. Joint Conf. on Neural Networks, 2009, p. 3321–3325.
    9. 9)
      • Fix, E., Hodges, J.L.: `Discriminatory analysis, nonparametric discrimination: consistency properties', Technical Report, 1951.
    10. 10)
    11. 11)
      • P.S. Addison . (2002) The illustrated wavelet transform handbook.
    12. 12)
    13. 13)
      • R. Acharyya . (2008) A new approach for blind source separation of convolutive sources – wavelet based separation using shrinkage function.
    14. 14)
      • T. Hastie , R. Tibshirani , J. Friedman . (2001) The elements of statistical learning: data mining, inference, and prediction.
    15. 15)
      • Guo, L., Rivero, D., Seoane, J.A., Pazos, A.: `Classification of EEG signals using relative wavelet energy and artificial neural networks', Proc. First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, 2009, Shanghai, China, p. 177–184.
    16. 16)
      • Mohseni, H.R., Maghsoudi, A., Shamsollahi, B.: `Seizure detection in EEG signals: a comparison of different approaches', 28thAnnual Int. Conf. IEEE Engineering in Medicine and Biology Society 2006, EMBS06, 2006, p. 6724–6727.
    17. 17)
    18. 18)
    19. 19)
      • D.E. Goldberg . (1999) Genetic algorithms in search, optimization and machine learning.
    20. 20)
      • M.R. Ahsan , M.I. Ibrahimy , O.O. Khalifa . EMG signal classification for human computer interaction: a review. Eur. J. Sci. Res. , 3 , 480 - 501
    21. 21)
    22. 22)
      • Rivero, D., Dorado, J., Rabuñal, J., Pazos, A.: `Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison', IEEE – INNS – ENNS Int. Joint Conf. on Neural Networks, 2009, p. 2685–2692.
    23. 23)
      • Schröder, M., Bogdan, M., Rosenstiel, W., Hinterberger, T., Birbaumer, N.: `Automated EEG feature selection for brain computer interfaces', Proc. First Int. IEEE EMBS Conf. on Neural Engineering, 2003, Capri Island, Italy.
    24. 24)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2010.0215
Loading

Related content

content/journals/10.1049/iet-spr.2010.0215
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address