title: Analysis of the automatic detection of critical epochs from coma-EEG by dominant components and features extraction.
abstract: Recent works showed that meaningful dominant components can be extracted from the EEG of patients in coma through an algorithm based on the joint use of Principal Component Analysis (PCA) and Independent Component Analysis (ICA). A procedure for automatic critical epoch detection would support the doctor in the long time monitoring of the patients, thus we investigated the automatic quantification of the criticality of the epochs. In this paper we propose a procedure based on the extraction of dominant components and features for the quantification of the critical state of each epoch, in particular we use entropy and kurtosis. This feature analysis allowed us to detect some epochs that are likely to be critical and that are worth being carefully inspected electrographically by the expert.
authors.affiliation: Department of Informatics, Mathematics, Electronics and Transportations, Mediterranea University of Reggio Calabria, via Graziella Feo di Vito Reggio Calabria, I-89060 Italy. giuseppina.inuso@unirc.it
authors.forename: Giuseppina
authors.initials: G
authors.lastname: Inuso
authors.forename: Fabio
authors.initials: F
authors.lastname: La Foresta
authors.forename: Nadia
authors.initials: N
authors.lastname: Mammone
authors.forename: F Carlo
authors.initials: FC
authors.lastname: Morabito
mesh_terms: D001331:Automation; D001921:Brain / Q000473:pathology; D003128:Coma / Q000175:diagnosis* / Q000473:pathology; D003627:Data Interpretation, Statistical; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D001330:Electronic Data Processing; D019277:Entropy; D004867:Equipment Design; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors