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The University of Southampton

Research project: Application of Data Fusion techniques in identification of sub-clinical Polysomnographic events in children with Sleep Disordered Breathing

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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.

We specifically focus on respiratory cycle related EEG changes (RCREC) and aim to understand its physiological origins and explore its potential connection with the obstructive sleep apnoea (OSA) syndrome. RCREC has been shown to predict sleepiness in patients with OSA and is hence speculated to be a clinically significant phenomenon. We have also looked into how respiratory cycle segmentation can affect RCREC and what its implications are on day-time correlates of RCREC.

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