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Estimation of Autocorrelation Space for Classification of Bio-medical Signals

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

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Abstract

Classification of biomedical signals is a complex task, but the analysis is very useful in medical diagnosis. In this paper we estimate the autocorrelation matrix of some brain signal by embedding the autocorrelation cone using Linear Matrix Inequalities (LMI). The minimum sample window has been chosen for the improved computational complexity. The partitioning of the space has been carried out using support vector machines. This method has been tested on different EEG signals recorded on subjects performing a multiplication, thought for composition of a song. The base signature has been recorded while the subject apparently was not doing anything.

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References

  1. Huang, W.-Y., Shen, X.-Q., Wu, Q.: Classifying the number of EEG current sources using support vector machines. In: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 4, pp. 1793–1795 (November 2002)

    Google Scholar 

  2. Garcia, G.N., Ebrahimi, T., Vesin, J.M.: Support vector EEG classification in the Fourier and time-frequency correlation domains. In: Proceedings of First IEEE EMBS Conference on Neural Engineering, pp. 591–594 (March 2003)

    Google Scholar 

  3. Lee, H., Choi, S.: PCA-based linear dynamical systems for multichannel EEG classification. In: Proceedings of the 9th ICONIP 2002, vol. 2, pp. 745–749 (November 2002)

    Google Scholar 

  4. Guyon, Elisseeff, A.: An introduction to variable and feature selection. J. Machine Learning Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  5. Müller, K.-R., Anderson, C.W., Birch, G.E.: Linear and Nonlinear Methods for Brain–Computer Interfaces. IEEE Trans. on Neural Systems and Rehabilitation Engineering 11(2), 165 (2003)

    Article  Google Scholar 

  6. Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. on Neural Systems and Rehabilitation 6(3), 316–325 (1998)

    Article  Google Scholar 

  7. Mohanty, M.N., Routray, A., Kabisatpathy, P.: Optimization of Features using Evolutionary Algorithm for EEG Signal Classification. Int. J. Computational Vision and Robotics 1(3), 297–310 (2010)

    Article  Google Scholar 

  8. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  9. Groutage, D., Bennink, D.: A new matrix decomposition based on optimum transformation of the singular value decomposition basis sets yields principal features of time-frequency distributions. In: Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (August 2000)

    Google Scholar 

  10. Popovici, V., Thiran, J.P.: Pattern recognition using higher-order local autocorrelation coefficients. In: 12th IEEE Workshop on Neural Networks for Signal Processing, September 4-6, pp. 229–238 (2002)

    Google Scholar 

  11. Luo, Z.-Q.: Applications of convex optimization in signal processing and digital communication. Math. Program, Ser. B 97, 177–207 (2003)

    Article  MATH  Google Scholar 

  12. William Helton, J., McCullough, S., Putinar, M., Vinnikov, V.: Convex Matrix Inequalities Versus Linear Matrix Inequalities. IEEE Trans. on Automatic Control 54(5), 952–964 (2009)

    Article  Google Scholar 

  13. Haykins, S.: Neural Networks, 2nd edn. Prentice Hall (1999)

    Google Scholar 

  14. Sathiya Keerthi, S., Chapelle, O., DeCoste, D.: Building Support Vector Machines with Reduced Classifier Complexity. Journal of Machine Learning Research 7, 1493–1515 (2006)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Mihir Narayan Mohanty

  2. Department of Electrical Engineering, IIT Kharagpur, WB, India

    Aurobinda Routray

Authors
  1. Mihir Narayan Mohanty
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  2. Aurobinda Routray
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

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© 2012 Springer-Verlag Berlin Heidelberg

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Mohanty, M.N., Routray, A. (2012). Estimation of Autocorrelation Space for Classification of Bio-medical Signals. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_81

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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