Complexity analysis of spontaneous EEG

Abstract

The aim of the present paper is the assessment of the overall complexity of spontaneous and non-paroxysmal EEG signals obtained from three groups of human subjects, e.g., healthy, seizure and mania. Linear complexity measure suitable for multi-variate signals, along with nonlinear measures such as approximate entropy (ApEn) and Taken's estimator are considered. The degree of linear complexity is significantly reduced for the pathological groups compared with healthy group. The nonlinear measures of complexity are significantly decreased in the seizure group for most of the electrodes, whereas a distinct discrimination between the maniac and healthy groups based on these nonlinear measures is not evident.