Three novel spike detection approaches for noisy neuronal data

Azami, H and Sanei, S (2012) Three novel spike detection approaches for noisy neuronal data

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Abstract

In this paper three new approaches based on smoothed nonlinear energy operator (SNEO), fractal dimension (FD) and standard deviation to detect the spikes for noisy neuronal data are proposed. In many cases, especially when there are several noise sources, these methods may not be acceptable as spike detectors. To overcome these problems, we use Savitzky-Golay filter and discrete wavelet transform (DWT) as pre-processing steps. Results show that when there is too much noise in the signal, the proposed method using the standard deviation and DWT can detect the spikes better than the other methods. The average detection rate and false detection of spikes for the proposed method based on standard deviation and DWT are respectively 100% and 43% for semireal signals with SNR=-5 dB. © 2012 IEEE.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID Azami, H Sanei, S s.sanei@surrey.ac.uk
Date :	1 December 2012
DOI :	10.1109/ICCKE.2012.6395350
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 12:42
Last Modified :	23 Jan 2020 18:02
URI:	http://epubs.surrey.ac.uk/id/eprint/836393

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