Probabilistic graphical model identifies clusters of EEG patterns in recordings from neonates

Abstract Objectives In this paper we introduce a novel method for the evaluation of neonatal brain function via multivariate EEG (electroencephalography) signal processing and embedding into a probabilistic graph, the so called Chow-Liu tree. Methods Using 28 EEG recordings of preterm and term neonate infants the complex features of the EEG signals were constructed in the form of a Chow-Liu tree. The trees were embedded into a 3 dimensional Euclidean space. Clustering of specific EEG patterns was done by complete linkage algorithm. Results Our analytic tool was able to build clusters of patients with pathological EEG findings. In particular, we were able to make a visual proof on a 3d multi…