Spike detection approaches for noisy neuronal data: Assessment and comparison

Spike detection in extracellular recordings is a difficult problem, especially when there are several noise sources. In this paper, three new approaches based on fractal dimension (FD), smoothed nonlinear energy operator (SNEO) and standard deviation to detect the spikes for noisy neuronal data are proposed. These methods however do not perform well in some cases, especially when the noise level is high. To overcome these problems, we use five smoothing techniques, namely, discrete wavelet transform (DWT), Kalman filter (KF), singular spectrum analysis (SSA), Savitzgy-Golay filter, and empirical mode decomposition (EMD). Although filtering approach based on EMD is relatively slow, when SNRs>0 dB, those approaches which use EMD have the best efficiency and accuracy. While SNRs<0 dB, it is demonstrated that for SSA followed by SNEO, the performance in terms of the average spikes detection accuracy and CPU time is the most desirable.