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Title: Application of signal processing to respiratory cycle related EEG change (RCREC) in children
Author: Motamedi Fakhr, Shayan
ISNI:       0000 0004 5349 274X
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2014
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Sleep is an important part of everyday life. It directly affects daytime cognition and general performance. In children, sleep is a crucial requirement for growth and learning and lack of sleep may manifest itself as a long lasting developmental deficit. Sleep disorders which disrupt the normal continuity of sleep therefore benefit from early identification and treatment. A common cause of sleep disruption is sleep disordered breathing which can be associated with frequent arousals from sleep. Many relevant areas of sleep research continue to generate new and interesting findings utilising biosignals such as EEGs. Respiratory cycle related EEG change (RCREC) is a good example of this. The method for quantification of RCREC relies on the appropriate application of signal processing and the signals involved in the procedure are polysomnographic. Furthermore, RCREC is thought to reflect morbid micro-arousals in sleep and is hence also of clinical importance. Given that the field of RCREC research is a recently established one, there is much room for constructive investigation. The current state of RCREC research is therefore expanded in this thesis. The method for calculation of respiratory cycle related EEG change (RCREC) is replicated and expanded in this project. Shortcomings of the method have been identified and accounted for where appropriate. In particular, the sensitivity of RCREC to airflow signal segmentation is addressed and alternative segmentation approaches are suggested. The general influence of airflow segmentation on RCREC is investigated and a mathematical explanation for RCREC sensitivity is given. Additionally, the ability of RCREC related parameters to predict daytime cognitive functions is assessed. Results suggest that RCREC parameters are capable of predicting quality of episodic memory, power (speed) of attention and internal processing speed.
Supervisor: Moshrefi-Torbati, Mohamed ; Hill, Martyn ; Hill, Catherine Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QP Physiology ; RJ101 Child Health. Child health services ; TK Electrical engineering. Electronics Nuclear engineering