The use of continuous quantitative electroencephalography (qEGG) monitoring in predicting ischemia & course of recovery in the acute ischemic stroke patient- correlation with neuroimaging data. Abstract: Background: Quantitative Electroencephalography (qEEG), a non-invasive recording technique has been considered as an alternative to advanced imaging (CT & MRI) due to its ability to detect early cerebral ischemia. The main objective of this systematic review is to determine the diagnostic accuracy of qEEG, to examine its validity and compare it to other imaging modalities across studies. Methods: Case-control (English and non-English) studies published in the last 20 years up until December 2015 will be identified using Medline, EMBASE, PubMed, Google Scholar, Scopus and Cochrane databases. Articles that discuss qEEG methodology against any advanced imaging tool (CT(a), MR(a)/DWI, DUS, DSA or CT perfusion) in patients with a final diagnosis of Acute Ischemic Stroke will be included. Data will be extracted onto piloted data extraction forms and the Methodological Quality of the final list of included studies will be determined through the QUADAS-2 tool. Microsoft Excel (2007) will be used to calculate diagnostic accuracy measures, sROC curve, forest plots, Cohen's d effect size and Chronbach's alpha of all studies and across studies (where required), and correlations (between each EEG parameter and corresponding imaging data) with their significance, will be determined through Spearman's/Pearson's (boot) correlations and t-values on MatLab software. Results: Conclusion: This systematic review will elucidate the diagnostic performance of the qEEG in detecting early cerebral ischemia and observe whether a specific EEG parameter is identified that can be associated with the ischemic lesion seen on advanced imaging. Project type: Systematic review Meta-analysis Imaging keywords: Computed tomography (CT) Magnetic resonance (MR) Electroencephalography (EEG) Application / disease keywords: Stroke Supervisor(s): Dr Cyril Pernet Programme: Neuroimaging for research MSc Year: 15-16 This article was published on 2024-08-22
