Abstract
Objective: We implemented and tested an existing seizure detection
algorithm for scalp EEG (sEEG) with the purpose of improving it to
intracranial EEG (iEEG) recordings.
Method: iEEG was obtained from 16 patients with focal epilepsy
undergoing work up for resective epilepsy surgery. Each patient had 4 or 5
recorded seizures and 24 hours of non-ictal data were used for evaluation.
Data from three electrodes placed at the ictal focus were used for the
analysis. A wavelet based feature extraction algorithm delivered input to
a support vector machine (SVM) classifier for distinction between ictal
and non-ictal iEEG. We compare our results to a method published by
Shoeb in 2004. While the original method on sEEG was optimal with the
use of only four subbands in the wavelet analysis, we found that better
seizure detection could be made if all subbands were used for iEEG.
Results: When using the original implementation a sensitivity of 92.8%
and a false positive ratio (FPR) of 0.93/h were obtained. Our extension of
the algorithm rendered a 95.9% sensitivity and only 0.65 false detections
per hour.
Conclusion: Better seizure detection can be performed when the
higher frequencies in the iEEG were included in the feature extraction.
Our future work will concentrate on development of a method for
identification of the most prominent nodes in the wavelet packets analysis
for optimization of an automatic seizure detection algorithm.
Original language | English |
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Journal | Clinical Neurophysiology |
Volume | 121 |
Issue number | Supplement 1 |
Pages (from-to) | S246 |
ISSN | 1388-2457 |
DOIs | |
Publication status | Published - 2010 |
Event | International Congress of Clinical Neurophysiology - Kobe, Japan Duration: 1 Jan 2010 → … Conference number: 29 |
Conference
Conference | International Congress of Clinical Neurophysiology |
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Number | 29 |
City | Kobe, Japan |
Period | 01/01/2010 → … |