Feature extraction using wavelet and fractal
Pattern Recognition Letters
Savitzky-Golay smoothing and differentiation filter for even number data
Signal Processing
A time--frequency approach for noise reduction
Digital Signal Processing
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
An advanced spike detection and sorting system
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Smoothing and thresholding in neuronal spike detection
Neurocomputing
A nonlinear time-frequency analysis method
IEEE Transactions on Signal Processing
A hybrid evolutionary approach to segmentation of non-stationary signals
Digital Signal Processing
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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 SNRs0dB, those approaches which use EMD have the best efficiency and accuracy. While SNRs