Spectral subtraction and cepstral distance for enhancing EEG entropy

Electroencephalographic (EEG) signals are normally acquired in the presence of background noise which causes inaccurate or false entropy measurement throughout the signal. In this paper, spectral subtraction is used to pre-process EEG signals to improve the accuracy of computing the subband wavelet entropy (SWE). The silent period in the EEG signal is identified via cepstral distance which allows its entropy to be set to zero. The EEG signal presented in this paper represents various stages of brain recovery obtained from a rodent following global cerebral ischemia. The various subband entropies are calculated using wavelet decomposition in EEG subbands, namely Delta, Theta, Alpha, Beta and Gamma. The utilization of spectral subtraction improved the accuracy of the SWE as compared to energy thresholding.