CLASSIFICATION OF NEURONAL ACTION POTENTIALS USING WAVELET-TRANSFORM
Cite this article as:
Dumsky . V., Pavlov A. N., Tupitsyn А. N., Makarov V. А. 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
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 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 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.
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BibTeX
author = { D. V. Dumsky and A. N. Pavlov and А. N. Tupitsyn and V. А. Makarov},
title = {CLASSIFICATION OF NEURONAL ACTION POTENTIALS USING WAVELET-TRANSFORM},
year = {2005},
journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
volume = {13},number = {6},
url = {https://old-andjournal.sgu.ru/en/articles/classification-of-neuronal-action-potentials-using-wavelet-transform},
address = {Саратов},
language = {russian},
doi = {10.18500/0869-6632-2005-13-5-77-98},pages = {77--98},issn = {0869-6632},
keywords = {-},
abstract = {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 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 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. }}