Visualization methods used for evaluation of neonatal polysomnographic data

Polysomnographic (PSG) signal processing represents a complex process consisting of several subsequent steps, namely pre-processing, segmentation, extraction of descriptive features, and classification. In this paper we focus on visualization methods that are also unseparable part of the whole process. The aim of these methods is to ease the work of medical doctors and to show trends that are not obvious when performing a manual inspection of the recorded signal. In this study, the designed methods are applied to neonatal PSG data and enable the enhancement in visual differentiation between three important behavioral states: quiet sleep (QS), active sleep (AS) and wakefulness (WK). The ratio of these states is a significant indicator of the maturity of the newborn brain in clinical practice.