Research project: Application of Data Fusion techniques in identification of sub-clinical Polysomnographic events in children with Sleep Disordered Breathing
Polysomnography (PSG) is known as the gold standard method in sleep monitoring. It is a very rich data set containing both physiological and diagnostic information. The general aim of this project is to indentify/develop/combine signal processing techniques which can be effectively applied to PSG data sets in order to detect or analyse important events in sleep such as arousals or micro arousals. More specifically, we focus on analysing respiratory cycle related EEG changes (RCREC), a new phenomenon characterised by statistically significant changes in EEG power in different stages of respiration which may be a manifest of numerous micro arousals.