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Title: A spatial-temporal decomposition of the EEG : derivation, validation and clinical application
Author: Jackson, C. D.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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A novel method for the decomposition of the EEG into its constituent parts, the Short Epoch Dominant Activity Clustering Algorithm (SEDACA), is described along with its validation and clinical application. The method’s derivation, from its roots in a simple eye-correction technique, to the final algorithm, is described. The results at various stages of the method are illustrated by tracings from real EEG recordings. Validation of the method is provided through the application of SEDACA to a large number (>100,000) of simulated EEG records. The EEGs were simulated to combine various mixtures of brain signals and artefacts, in known ratios on the channels of the recording, allowing measurement of the accuracy of the decomposition performed by SEDACA. Hepatic encephalopathy (HE) is the term used to describe the neuropsychiatric abnormalities which arise in patients with cirrhosis. Many patients do not show clinically obvious signs of change but do show significant abnormalities of psychometric performance and/or changes in the EEG, predominantly slowing of the posterior rhythm. There is no gold standard for the diagnosis of HE but psychometric performance is used as an acceptable surrogate. Assessment of the EEG changes in these patients is conventionally undertaken by visual analysis of the EEG recording or else spectral analysis on the P3-P4 derivation but has low diagnostic sensitivity. In theory isolation of this EEG rhythm, from the other background rhythms of brain activity and from any artefact or noise signals in the recording, using SEDACA, should improve performance. Indeed use of SEDACA improved the sensitivity of the EEG, for the diagnosis of HE, using conventional thresholds from 49% to 58%; however, redefinition of the thresholds using ROC analysis improved the sensitivity to 82% and 86% respectively. Further modifications of the technique are suggested together with other possible forms of EEG analysis in this patient population.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available