Abstract
Several different algorithms have been proposed
for automatic detection of epileptic seizures based on both scalp
and intracranial electroencephalography (sEEG and iEEG).
Which modality that renders the best result is hard to assess
though. From 16 patients with focal epilepsy, at least 24 hours
of ictal and non-ictal iEEG were obtained. Characteristics of
the seizures are represented by use of wavelet transformation
(WT) features and classified by a support vector machine.
When implementing a method used for sEEG on iEEG data,
a great improvement in performance was obtained when the
high frequency containing lower levels in the WT were included
in the analysis. We were able to obtain a sensitivity of 96.4%
and a false detection rate (FDR) of 0.20/h. In general, when
implementing an automatic seizure detection algorithm made
for sEEG on iEEG, great improvement can be obtained if a
frequency band widening of the feature extraction is performed.
This means that algorithms for sEEG should not be discarded
for use on iEEG - they should be properly adjusted as
exemplified in this paper.
| Original language | English |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2010 |
| Publisher | IEEE |
| Publication date | 2010 |
| ISBN (Print) | 978-1-4244-4123-5 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010 Conference number: 32 http://embc2010.embs.org/ |
Conference
| Conference | 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| Number | 32 |
| Country/Territory | Argentina |
| City | Buenos Aires |
| Period | 31/08/2010 → 04/09/2010 |
| Internet address |