Multiresolution entropy measure for neuronal multiunit activity

It is known that the multiunit activity (MUA) reflects the status of population of neurons in the vicinity of an electrode. We provide a quantitative measure of the time-varying multiunit neuronal spiking activity using an entropy based approach. To verify the status embedded in the neuronal activity of a population of neurons, we incorporate the discrete wavelet transform (DWT) to isolate the inherent spiking activity of MUA from the noise and background cortical activity or field potentials. Owing to the decorrelating property of DWT, the spiking activity would be preserved while reducing the non-spiking component such as the background noise. By evaluating the entropy of the wavelet coefficients of the denoised MUA, a multiresolution entropy of the MUA signal is developed. The proposed entropy measure was tested in the analysis of both simulated noisy MUA and actual MUA recorded from cortex in rodent model which undergoes hypoxic-ischemic brain injury. Simulation and experimental results demonstrate that the dynamics of a population can be quantified by using the proposed multiresolution entropy.