ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Dumskij D. V., Pavlov A. N., Tupicyn A. N., Makarov V. V. Classification of neuronal action potentials using wavelet-transform. Izvestiya VUZ. Applied Nonlinear Dynamics, 2005, vol. 13, iss. 6, pp. 77-98. DOI: 10.18500/0869-6632-2005-13-5-77-98

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text:
(downloads: 90)
Article type: 

Classification of neuronal action potentials using wavelet-transform

Dumskij Dmitrij Viktorovich, Saratov State University
Pavlov Aleksej Nikolaevich, Saratov State University
Tupicyn Anatolij Nikolaevich, Saratov State University
Makarov Vladimir Vladimirovich, Saratov State University

In this paper, a comparative study of methods for classification of neuronal action potentials is performed, namely, the standard Principal Component Analysis (PCA) and techniques based on the wavelet-transform. It is shown that there are at least two cases when the wavelet-based approaches have advantages: 1) the presence of a small-scale structure in the shapes of spikes, and 2) the presence of slow noise of high intensity. It is stated that the quality of spike-sorting can be increased by signal’s filtering. The problem of choosing optimal wavelet-coefficients for spike classification is discussed. Proposed method is based on combination of the PCA and the wavelet-transform. Main idea of the method consists in the estimation of typical spike waveforms and in the use of those wavelet-coefficients that provide maximal distinctions between the chosen waveforms. The suggested approach allows us to reduce classification errors.

Key words: 
  1. Lewicki M. A review of methods for spike sorting: the detection and classification of neural potencials // Net. Com. Neu. Sys. 1998. Vol. 9. P. R53–R78.
  2. Harris K., Henze D., Csicsvari J., Hirase H., Buzsaki G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements // J. Neurophysiol. 2000. Vol. 84. P. 401–414.
  3. Schmidt E. Computer separations of multi-unit neuroelectric data: a review // J. Neurosci. Methods. 1984. Vol. 12. P. 95–111.
  4. Gray C., Maldonado P., Wilson M., McNaughton B. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex // J. Neurosci. Methods. 1995. Vol. 63. P. 43–54.
  5. Eggermont J., Epping W., Aertsen A. Stimulus dependent neural correlations in the auditory midbrain of the grassfrog (Rana temporaria L.) // Biol. Cybern. 1983. Vol. 47. P. 103–117.
  6. Salganicoff M., Sarna M., Sax L., Gerstein G. Unsupervised waveform classification for multi-neural recordings: a real-time, software based system. I. Algorithms and implementation // J. Neurosci. Methods. 1988. Vol. 25. P. 181–187.
  7. Sarna M., Gochin P., Kaltenbach J., Salganicoff M., Gerstein G. Unsupervised waveform classification for multi-neuron recordings: a real-time, software based system. II. Performance comparison to other sorters // J. Neurosci. Methods. 1988. Vol. 25. P. 189–196.
  8. Zouridakis G., Tam D. Multi-unit spike discrimination using wavelet transforms // Comput. Biol. Med. 1997. Vol. 27. P. 9–18.
  9. Hulata E., Segev R., Ben-Jacob E. A metod for spike sorting and detection based on wavelet packets and Shannon’s mutual information // J. Neurosci. Methods. 2002. Vol. 117. P. 1–12.
  10. Letelier J., Weber P. Spike sorting based on discrete wavelet transform coefficients // J. Neurosci. Methods. 2000. Vol. 101. P. 93–106.
  11. Quian Quiroqa R., Nadasdy Z., Ben-Shaul Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering // Neural Computation. 2004. Vol. 16. P. 1661–1687.
  12. Kim K., Kim S. A Wavelet-Based Method for Action Potential Detection From Extracellular Neural Signal Recording With Low Signal-to-Noise Ratio // IEEE Trans. on Biomed. Eng. 2003. Vol. 50, No 8. P. 999–1011.
  13. Simon W. The real-time sorting of neuro-electric action potentials in multiple unit studies Electroenceph // Clin. Neurophysiol. 1965. Vol. 18. P. 192–195.
  14. Feldman J., Roberge F. Computer detection and analysis of neuronal spike sequences // Inform. 1971. Vol. 9. P. 185–197.
  15. Dinning G. Real-time classification of multiunit neural signals using reduced feature sets // IEEE Trans. Biomed. Eng. 1981. Vol. 28. P. 804–812.
  16. Glaser E., Marks W. On-line separation of interleaved neuronal pulse sequences Data Acquisition Process // Biol. Med. 1968. Vol. 5. P. 137–156.
  17. Gerstein G., Bloom M., Espinosa I., Evanczuk S., Turner M. Design of a laboratory for multineuron studies// IEEE Trans. Systems,ManCybern. 1983. Vol. 13. P. 668–676.
  18. Press W.H., Teukolsky S.A., Vetterling W.T., Flanney B.P. Numerical Recipes in C: the art of scientific computing. Cambridge University Press, 1992.
  19. Burrus C.S., Gopinath R.A., Guo H. Introduction to Wavelets and Wavelet Transforms: A Primer. N.J: Prentice Hall, 1997.
  20. Chui C.K. Wavelets: A Mathematical Tool for Signal Analysis SIAM Monographs on Mathematical Modeling and Computation. Philadelphia, PA: SIAM, 1997.
  21. Астафьева Н.М. Вейвлет-анализ: основы теории и примеры применения // УФН. 1996. Т. 166, No 4. С. 1145–1170.
Short text (in English):
(downloads: 53)