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


Cite this article as:

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: https://doi.org/10.18500/0869-6632-2005-13-5-77-98

Language: 
Russian

Classification of neuronal action potentials using wavelet-transform

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

In this paper, a comparative study of methods for classi?cation 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 caseswhen 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 ?ltering. The problem of choosing optimal wavelet-coe?cients for spike classi?cation 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-coe?cients that provide maximal distinctions between the chosen waveforms. The suggested approach allows us to reduce classi?cation errors.

Key words: 
DOI: 
10.18500/0869-6632-2005-13-5-77-98
References: 

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