Three novel spike detection approaches for noisy neuronal data

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.