EEG signal segmentation using adaptive Markov process amplitude modeling

Adaptive Markov process amplitude modeling was used to simulate and segment EEG signals. The least mean square adaptive algorithm was used to estimate the parameters of a first order Markov model. The coefficients of the model were utilized for EEG signal segmentation. The EEG signals were recorded from a controlled experimental setup of rodent brain injury with hypoxic-ischemic cardiac arrest. Results demonstrated that the proposed technique is a potential tool for EEG signal analysis.