Removal of peak and spike noise in EEG signals based on the analytic signal magnitude

Peak and spike artifacts in time series represent a serious problem for signal analysis especially in biomedical field. From the last decades, different techniques have been used for their removal mainly based on adaptive filters. This work presents a new approach for removing peak and spike artifacts based on the analytic signal envelope, filtered with a low-pass filter. The proposed algorithm was tested on electroencephalogram signals containing peak and spike artifacts. Results showed that this method permitted to remove the peak and spike artifacts preserving both high correlation (ρ>0.9) and spectral coherence equation with the original signal.