Detection of interictal epileptiform discharge in EEG

Geerts, A.J.E. (2012) Detection of interictal epileptiform discharge in EEG.

Abstract:The diagnosis of epilepsy heavily depends on the detection of epileptiform discharges in interictal EEG, the EEG in between two seizures. By visual analysis a physician wants to detect these epileptiform discharges (spikes). Due to the wide variety of morphologies of epileptiform discharges, and their similarity to waves that are part of normal EEG or to artifacts, this detection is far from straightforward. Moreover, it is a time consuming task, holding back for the analysis of long-term recordings, which would improve the detection of evidence of epilepsy [17, 18]. In this study a first step has been made towards automated detection. We would like to find events with a heightened chance of being an epileptiform discharge. All other parts of the EEG can then be neglected, resulting in a reduction of the time needed to analyse a record. In this study we investigated two methods: wavelet analysis and matched filtering. The choice for wavelet analysis was motivated from literature. A big drawback of wavelet analysis turns out to be the limited choice for templates with which to correlate the signal. Therefore we propose to use matched filtering in which we are not restricted in the choice for templates. Classically, mathed filtering considers an event (spike) ‘detected’ if some correlation exceeds a certain threshold. We added a power threshold, claiming that the template has to explain for a certain percentage of the signal power before an event is considered to be of an epileptiform kind. This resulted in a sensitivity (percentage of true spikes that are detected) of 86.41% with 0.1503 False Positives per Minute (FPM) if this threshold was set to 75%. This is showed to be a lower bound for the data set, consisting of 10 EEG recordings, as we were able to obtain a sensitivity of 95.63% with an FPM of 0.2002 as well for slightly different threshold settings. This approach is not suitable for automation. It requires the selection of a suitable template before matched filtering can be applied, implying that the entire recording needs to be scanned first. It, however, shows the strength of matched filtering and the present with a library of spikes is therefore proposed for the goal of automated spike detection. Preliminary results, with a library of just 9 templates and a fairly simple rules defining an event as epileptiform or not, show this to be promising as we already reach sensitivities of around 80% with few false positives per minute.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
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