Advertisement

Analysis of Epileptic Activity Based on Brain Mapping of EEG Adaptive Time-Frequency Decomposition

  • Maximiliano Bueno-López
  • Pablo A. Muñoz-Gutiérrez
  • Eduardo Giraldo
  • Marta Molinas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

The applications of Empirical Mode Decomposition (EMD) in Biomedical Signal analysis have increased and is common now to find publications that use EMD to identify behaviors in the brain or heart. EMD has shown excellent results in the identification of behaviours from the use of electroencephalogram (EEG) signals. In addition, some advances in the computer area have made it possible to improve their performance. In this paper, we presented a method that, using an entropy analysis, can automatically choose the relevant Intrinsic Mode Functions (IMFs) from EEG signals. The idea is to choose the minimum number of IMFs to reconstruct the brain activity. The EEG signals were processed by EMD and the IMFs were ordered according to the entropy cost function. The IMFs with more relevant information are selected for the brain mapping. To validate the results, a relative error measure was used.

Keywords

Brain mapping Empirical mode decomposition Epilepsy Signal analysis 

Notes

Acknowledgment

This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme, also under the funding of the Departamento Administrativo Nacional de Ciencia, Tecnología e Innovación (Colciencias). Research project: 111077757982 “Sistema de identificación de fuentes epileptogénicas basado en medidas de conectividad funcional usando registros electroencefalográficos e imágenes de resonancia magnética en pacientes con epilepsia refractaria: apoyo a la cirugía resectiva” and also this work is also part of the research project”Solución del problema inverso dinámico considerando restricciones espacio-temporales no homogéneas aplicado a la reconstrucción de la actividad cerebral” funded by the Universidad Tecnológica de Pereira under the code E6-17-2.

References

  1. 1.
    Im, C., Seo, J.M.: A review of electrodes for the electrical brain signal recording. Biomed. Eng. Lett. 6(3), 104–112 (2016)CrossRefGoogle Scholar
  2. 2.
    Subha, D.P., Joseph, P.K., Acharya, U.R., Lim, C.M.: EEG signal analysis: a survey. J. Med. Syst. 34(2), 195–212 (2010)CrossRefGoogle Scholar
  3. 3.
    Lin, K.Y., Chen, D.Y., Tsai, W.J.: Face-based heart rate signal decomposition and evaluation using multiple linear regression. IEEE Sens. J. 16(5), 1351–1360 (2016)CrossRefGoogle Scholar
  4. 4.
    Bueno-Lopez, M., Giraldo, E., Molinas, M.: Analysis of neural activity from EEG data based on EMD frequency bands. In: 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Vol. 1, Batumi, Georgia, pp. 1–5. IEEE, December 2017Google Scholar
  5. 5.
    Men-Tzung, L., Kun, H., Yanhui, L., Peng, C., Vera, N.: Multimodal pressure-flow analysis: application of hilbert huang transform in cerebral blood flow regulation. EURASIP J. Adv. Signal Process. 2008(1), 1–15 (2008)zbMATHGoogle Scholar
  6. 6.
    Zhang, T., et al.: Multivariate empirical mode decomposition based sub-frequency bands analysis of the default mode network: a resting-state fmri data study. Appl. Inform. 2(1), 2 (2015)CrossRefGoogle Scholar
  7. 7.
    Giraldo-Suarez, E., Martinez-Vargas, J., Castellanos-Dominguez, G.: Reconstruction of neural activity from eeg data using dynamic spatiotemporal constraints. Int. J. Neural Syst. 26(07), 1–15 (2016)CrossRefGoogle Scholar
  8. 8.
    Plummer, C., Harvey, A.S., Cook, M.: EEG source localization in focal epilepsy: where are we now? Epilepsia 49(2), 201–218 (2008)CrossRefGoogle Scholar
  9. 9.
    Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89, 068102 (2002)CrossRefGoogle Scholar
  10. 10.
    Xiang, J., et al.: The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods 243(Suppl. C), 18–25 (2015)CrossRefGoogle Scholar
  11. 11.
    Wang, L., et al.: Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy 19(6), 3–17 (2017)Google Scholar
  12. 12.
    Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995 (1998)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Grech, R., et al.: Review on solving the inverse problem in EEG source analysis. J. NeuroEngineering Rehabil. 5(1), 25 (2008)CrossRefGoogle Scholar
  14. 14.
    Munoz, P., Giraldo, E.: Time-course reconstruction of neural activity for multiples simultaneous source. In: IFMBE Proceedings CLAIB 2016, Vol. 60, Bucaramanga, Colombia, pp. iv/485–iv/488. Springer, October 2016Google Scholar
  15. 15.
    Deering, R., Kaiser, J.F.: The use of a masking signal to improve empirical mode decomposition. In: Proceedings of (ICASSP 2005) IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 4, pp. iv/485–iv/488, March 2005Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Maximiliano Bueno-López
    • 1
  • Pablo A. Muñoz-Gutiérrez
    • 2
  • Eduardo Giraldo
    • 3
  • Marta Molinas
    • 4
  1. 1.Department of Electrical EngineeringUniversidad de la SalleBogotáColombia
  2. 2.Electronic Instrumentation TechnologyUniversidad del QuindíoArmeniaColombia
  3. 3.Department of Electrical EngineeringUniversidad Tecnológica de PereiraPereiraColombia
  4. 4.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations