Advertisement

Feature Extraction of High-Frequency Patterns with the a Priori Unknown Parameters in Noised Electrograms Using Spectral Entropy

  • Nikolay E. Kirilenko
  • Igor V. Shcherban’
  • Andrey A. Kostoglotov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

Abstract

Statement of the problem: the article looks into a class of problems which require identification of hidden regularities in adjustment of the bioelectrical activity of living organisms registered against various stimuli, through search and temporal localization of patterns in noised electrograms containing useful information. One of the approaches to solving such problems is based on an analysis of the Shannon entropy calculated based on the components of the power spectrum and called the spectral entropy function. It is found that under the conditions providing that the patterns in question pertain to high-frequency rhythms, and the boundaries of their energy spectra are a priori unknown, the criterial functions of spectral entropy are of low sensitivity. Purpose of the research: to develop cost functions of entropy analysis which are sufficiently sensitive for searching for high-frequency patterns with a priori unknown parameters in noised electrograms. Results: development of a cost function that makes it possible to find the frequency sub-band where the spectral components of the patterns in question maximally contribute to the total spectrum power. The subsequent computation of the spectral entropy in the identified frequency sub-band provides a solution to the problem of search for the response patterns in noised electrograms under the above conditions. Practical significance: the results confirm the effectiveness of the developed functions whose use is limited by the requirement that the electrogram should be recorded on more than one lead.

Keywords

Electrogram High-frequency pattern Shannon function Spectral entropy Local frequency sub-band 

References

  1. 1.
    Rangayyan, R.M.: Biomedical Signal Analysis: A Case-Study Approach. Wiley-IEEE Press, New York (2007)Google Scholar
  2. 2.
    Giannakakis, G.A., Tsiaparas, N.N., Xenikou, M.S., Papageorgiou, C., Nikita, K.S.: Wavelet entropy differentiations of event related potentials in Dyslexia. In: 8th IEEE International Conference on Bioinformatics and Bioengineering, pp. 1–6. IEEE Greece (2008)Google Scholar
  3. 3.
    Gorshkov, A.A., Osadchi, A.E., Fradkov, A.L.: Regularization of EEG/MEG inverse problem with a local cortical wave pattern. Inf. Control. Syst. 5(90), 12–20 (2017)Google Scholar
  4. 4.
    Cvetkov, O.V.: Entropiinyi analiz dannykh v fizike, biologii i tekhnike (Entropy Data Analysis in Physics, Biology and Technique). SPbGETU “LETI” Publ., Saint-Petersburg (2015)Google Scholar
  5. 5.
    Zunino, L., Perez, D.G., Garavaglia, M., Rosso, O.A.: Wavelet entropy of stochastic processes. Phys. A 379, 503–512 (2007)CrossRefGoogle Scholar
  6. 6.
    Hong, H., Yonghong, T., Yuexia, W.: Optimal base wavelet selection for ECG noise reduction using a comprehensive entropy criterion. Entropy 17, 6093–6109 (2015)CrossRefGoogle Scholar
  7. 7.
    Inouve, T., Shinosaki, K., Sakamoto, H., Inouye, T., Shinosaki, K., Sakamoto, H., Toi, S., Ukai, S., Iyama, A., Katsuda, Y., Hirano, M.: Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr. Clin. Neurophysiol. 3(79), 204–210 (1991)CrossRefGoogle Scholar
  8. 8.
    Viertio-Oja, H., Maja, V., Sarkela, M., Talja, P., Tenkanen, N., Tolvanen-Laakso, H., Paloheimo, M., Vakkuri, A., Yli-Hankala, A., Merilainen, P.: Description of the entropy algorithm as applied in the Datex-Ohmeda entropy module. Acta Anaesthesiol. Scand. 48, 154–161 (2004)CrossRefGoogle Scholar
  9. 9.
    Kekovic, G., Stojadinovic, G., Martac, L., Podgorac, J., Sekulic, S., Culic, M.: Spectral and fractal measures of cerebellar and cerebral activity in various types of Anesthesia. Acta Neurobiol. Exp. 70, 67–75 (2010)Google Scholar
  10. 10.
    Ostanin, S.A., Filatova, E.V.: A virtual instrument for spectral entropy estimation of heart rate. Izvestiia Altaiskogo gosudarstvennogo universiteta 1, 45–51 (2016)Google Scholar
  11. 11.
    Mirzaei, A., Ayatollahi, A., Gifani, P., Salehi, L.: Spectral entropy for epileptic seizures detection. In: 2nd International Conference on Computational Intelligence, pp. 301–307. Communication Systems and Networks, UK (2010)Google Scholar
  12. 12.
    Polat, K., Gunes, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Kannathal, N., Choo, M.L., Acharya, U.R.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187–194 (2005)CrossRefGoogle Scholar
  14. 14.
    Zhang, A., Yang, B., Huang, L.: Feature extraction of EEG signals using power spectral entropy. In: Internal Conference on BioMedical Engineering and Informatics, pp. 435–439. IEEE, China (2008)Google Scholar
  15. 15.
    Graham, D.J.: On the spectral entropy of thermodynamic paths for elementary systems. Entropy 11, 1025–1041 (2009)CrossRefGoogle Scholar
  16. 16.
    Misrihanov, A.M.: Primenenie metodov vejvlet-preobrazovanija v jelektro-jenergetike (Appliance of Wavelet-Transform Methods in Electrical Power Industry). Autom. Remote. Control. 5, 5–23 (2006)Google Scholar
  17. 17.
    Cyplihin, A.I., Sorokin, V.N.: Segmentacija rechi na kardinal’nye jelementy (Speech signal segmentation on cardinal elements). Informacionnye processy 6(3), 177–207 (2006)Google Scholar
  18. 18.
    Zahezin, A.M.: Metod nerazrushajushhego kontrolja dlja opredelenija zarozhdajushhihsja defektov pri pomoshhi Fourier i vejvlet-analiza vibracionnogo signala (Method of the non-destructive control for defects identification based on Fourier and wavelet analysis of vibration-signal), Vestnik Juzhno-Ural’skogo gosudarstvennogo universiteta (J. South. Ural. State Univ.) 13(2), 28–33 (2013)Google Scholar
  19. 19.
    Toh, A.M., Togneri, R., Nordholm, S.: Spectral entropy as speech features for speech recognition. In: Electrical Engineering and Computer Science, PEECS, Australia, pp. 22–25 (2005)Google Scholar
  20. 20.
    Jia, C., Xu, B.: An improved entropy-based endpoint detection algorithm. In: International Symposium on Chinese Spoken Language Processing, ISCSLP, Taiwan, pp. 96–97 (2002)Google Scholar
  21. 21.
    Zhang, Y., Ding, Y.: Voice activity detection algorithm based on spectral entropy analysis of sub-frequency band. BioTechnology Indian J. 10(20), 12342–12348 (2014)Google Scholar
  22. 22.
    Misra, H., Ikbal, S., Bourlard, H., Hermansky H.: Spectral entropy based feature for robust ASR. In: International Conference on Acoustics, Speech, and Signal Processing, pp. 2–6. IEEE, Canada (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikolay E. Kirilenko
    • 1
  • Igor V. Shcherban’
    • 2
  • Andrey A. Kostoglotov
    • 1
  1. 1.Rostov State Transport UniversityRostov-on-DonRussia
  2. 2.Southern Federal UniversityRostov-on-DonRussia

Personalised recommendations