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Improving Ensemble Averaging by Epoch Detrending in Evoked Potentials

  • Idileisy Torres-Rodríguez
  • Carlos A. FerrerEmail author
  • Ernesto Velarde-Reyes
  • Alberto Taboada-Crispi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The objective of this work is to evaluate different detrending methods in the quality of auditory evoked responses. We compared the average responses obtained by simply removing the DC level and the linear trend, and also the estimated trends using polynomials and Fourier models up to the 8th order. Two quality measures were used to compare the results: the standard deviation ratio, as a measure of the signal-to-noise ratio, and the correlation coefficient between consecutive responses obtained under the same experimental conditions. The best results were obtained using a polynomial model of order 7.

Keywords

Detrending Ensemble averages Evoked potential CCR SDR 

Notes

Acknowledgements

This work was partially supported by the Cuban National Program of Creation of an R+D Platform in Neuro-technology and by an Alexander von Humboldt Foundation Fellowship granted to C. A. Ferrer-Riesgo (Ref 3.2-1164728-CUB-GF-E).

References

  1. 1.
    Sörnmo, L., Laguna, P.: Bioelectrical Signal Processing in Cardiac and Neural Applications. Academic Press, London (2005)Google Scholar
  2. 2.
    Paulraj, M.P., et al.: Auditory evoked potential response and hearing loss: a review. Open Biomed. Eng. J. 9, 17–24 (2015)CrossRefGoogle Scholar
  3. 3.
    de Cheveigné, A., Arzounian, D.: Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data. NeuroImage 172(2018), 903–912 (2018)CrossRefGoogle Scholar
  4. 4.
    Kugiumtzis, D., Tsimpiris, A.: Measures of Analysis of Time Series (MATS): a MATLAB toolkit for computation of multiple measures on time series data bases. J. Stat. Softw. 33(i.5) (2010)Google Scholar
  5. 5.
    Casula, E.P., et al.: TMS-evoked long-lasting artefacts: a new adaptive algorithm for EEG signal correction. Clin. Neurophysiol. 128(9), 1563–1574 (2017)CrossRefGoogle Scholar
  6. 6.
    Taboada-Crispi, A., Lorenzo-Ginori, J.V., Lovely, D.F.: Adaptive line enhancing plus modified signal averaging for ventricular late potential detection. Electron. Lett. 35(16), 1293–1295 (1999)CrossRefGoogle Scholar
  7. 7.
    Taboada-Crispi, A.: Improving ventricular late potentials detection effectiveness. Doctoral dissertation, Ph.D. thesis, University of New Brunswick, Canada (2002)Google Scholar
  8. 8.
    Laguna, P., Sörnmo, L.: Sampling rate and the estimation of ensemble variability for repetitive signals. Med. Biol. Eng. Comput. 38(5), 540–546 (2000)CrossRefGoogle Scholar
  9. 9.
    Kotas, M., Pander, T., Leski, J.M.: Averaging of nonlinearly aligned signal cycles for noise suppression. Biomed. Signal Process. Control 21, 157–168 (2015)CrossRefGoogle Scholar
  10. 10.
    ACNS: Guideline 9C: guidelines on short-latency auditory evoked potentials. Am. Clin. Neurophysiology Soc. Guidel. 46(3), 275–286 (2006)Google Scholar
  11. 11.
    Ireland, K.H.: Can the auditory late response indicate audibility of speech sounds from hearing aids with different digital processing strategies. Doctoral dissertation, Ph.D. thesis, University of Southampton, United Kingdom (2014)Google Scholar
  12. 12.
    Rasheed, H.: A maximum likelihood method to estimate EEG evoked potentials. Doctoral dissertation, Ph.D. thesis, McGill University, Canada (1985)Google Scholar
  13. 13.
    Effern, A., Lehnertz, K., Schreiber, T., Grunwald, T., David, P., Elger, C.E.: Nonlinear denoising of transient signals with application to event related potentials. Physica D 140(3–4), 257–266 (2000)CrossRefGoogle Scholar
  14. 14.
    Davila, C.E., Mobin, M.S.: Weighted averaging of evoked potentials. IEEE Trans. Biomed. Eng. 39(4), 338–345 (1992)CrossRefGoogle Scholar
  15. 15.
    Khashei, M., Bijari, M.: A new class of hybrid models for time series forecasting. Expert Syst. Appl. 39(4), 4344–4357 (2012)CrossRefGoogle Scholar
  16. 16.
    Pander, T., Przybyla, T., Czabanski, R.: An application of the L P-norm in robust weighted averaging of biomedical signals. J. Med. Informatics Technol. 22(2), 1–8 (2013)Google Scholar
  17. 17.
    de Weerd, J.P.C.M.: Estimation of evoked potentials: a study of a posteriori ‘Wiener’ filtering and its time varying generalization. Doctoral dissertation, Ph.D. thesis, Catholic University of Nijmegen, The Netherlands (1981)Google Scholar
  18. 18.
    Ting, C.M., Salleh, S.H., Zainuddin, Z.M., Bahar, A.: Artifact removal from single-trial ERPs using non-Gaussian stochastic volatility models and particle filter. IEEE Signal Process. Lett. 21(8), 923–927 (2014)CrossRefGoogle Scholar
  19. 19.
    Silva, I.: Estimation of postaverage SNR from evoked responses under nonstationary noise. IEEE Trans. Biomed. Eng. 56(8), 2123–2130 (2009)CrossRefGoogle Scholar
  20. 20.
    Ferrer, C.A., González, E., Hernández-Díaz, M.E.: Correcting the use of ensemble averages in the calculation of harmonics to noise ratios in voice signals. J. Acoust. Soc. Am. 118(2), 605–607 (2005)CrossRefGoogle Scholar
  21. 21.
    Ferrer, C., González, E., Hernández-Díaz, M.E., Torrer, D., del Toro, A.: Removing the influence of shimmer in the calculation of harmonics-to-noise ratios using ensemble-averages in voice signals. EURASIP J. Adv. Signal Process. 2009, 1–7 (2009)CrossRefGoogle Scholar
  22. 22.
    Gopinath, K.S.: Reduction of noise due to task correlated motion in event related overt word generation functional magnetic resonance imaging paradigms, University of Florida (2003)Google Scholar
  23. 23.
    Ozaki, T.: Time Series Modeling of Neuroscience Data. CRC Press, Boca Raton (2012)CrossRefGoogle Scholar
  24. 24.
    Davé, R.N., Krishnapuram, R.: Robust clustering methods: a unified view. IEEE Trans. Fuzzy Syst. 5(2), 270–293 (1997)CrossRefGoogle Scholar
  25. 25.
    Tarvainen, M.P., Ranta-aho, P.O., Karjalainen, P.A.: An advanced detrending method with application to HRV analysis. EEE Trans. Biom. Eng. 49(2), 172–175 (2002)CrossRefGoogle Scholar
  26. 26.
    Gharieb, R.R., Cichocki, A.: Noise reduction in brain evoked potentials based on third-order correlations. IEEE Trans. Biomed. Eng. 48(5), 501–512 (2001)CrossRefGoogle Scholar
  27. 27.
    Cabana-Pérez, I.M., Velarde-Reyes, E., Torres-Fortuny, A., Eimil-Suarez, E., García-Giró, A.: Automatic ABR detection at near-threshold intensities combining template-based approach and energy analysis. In: Torres, I., Bustamante, J., Sierra, D. (eds.) VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016. IP, vol. 60, pp. 122–125. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-4086-3_31CrossRefGoogle Scholar
  28. 28.
    Doubell, T.P., et al.: The effect of interaural timing on the posterior auricular muscle reflex in normal adult volunteers. PLoS ONE 13(4), e0194965 (2018)CrossRefGoogle Scholar
  29. 29.
    Gregory, L., Rosa, R.F.M., Zen, P.R.G., Sleifer, P.: Auditory evoked potentials in children and adolescents with down syndrome. Am. J. Med. Genet. Part A 176(1), 68–74 (2018)CrossRefGoogle Scholar
  30. 30.
    Picton, T.W., Hillyard, S.A., Krausz, H.I., Galambos, R.: Human auditory evoked potentials. I: evaluation of components. Electroencephalogr. Clin. Neurophysiol. 36, 79–190 (1974)Google Scholar
  31. 31.
    British Columbia Early Hearing Programme: Audiology Assessment Protocol. V. 4.1 (2012). http://www.phsa.ca/Documents/bcehpaudiologyassessmentprotocol.pdf. Accessed 10 May 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Informatics Research CenterUniversidad Central “Marta Abreu” de Las VillasSanta ClaraCuba
  2. 2.Pattern Recognition LabFriedrich Alexander University Erlangen-NurembergErlangenGermany
  3. 3.Electronics DepartmentCuban Neurosciences Center, CNEUROHavanaCuba

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