A new complexity-based algorithmic procedures for electroencephalogram (EEG) segmentation

Electroencephalogram (EEG) signals are complex and non-stationary. To analyze and model EEG records the segmentation of the signal into (quasi)- stationary intervals is needed. In this paper we propose a novel approach for the segmentation problem that occurs in “long” EEG records. This approach utilizes the concept of complexity of a continuous function. The complexity of a continuous function is defined as the fraction of the function values necessary to recover the original function via a certain fixed family of approximation methods without exceeding a given error. We applied this approach to the EEG signal of neonates to identify sleep stages. Our results showed 82-86% average agreement with the manual scoring provided by an expert pediatric neurologist.