Abstract
Several different algorithms have been proposed for automatic detection of epileptic seizure 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 |
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| Journal | I E E E Engineering in Medicine and Biology Society. Conference Proceedings |
| Volume | 2010 |
| Pages (from-to) | 2431-4 |
| Number of pages | 4 |
| ISSN | 2375-7477 |
| DOIs | |
| Publication status | Published - 31 Aug 2010 |
| Event | Annual International Conference of the IEEE 2010: Engineering in Medicine and Biology Society (EMBC) - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010 |
Conference
| Conference | Annual International Conference of the IEEE 2010 |
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| Country/Territory | Argentina |
| City | Buenos Aires |
| Period | 31/08/2010 → 04/09/2010 |
Keywords
- Algorithms
- Automatic Data Processing
- Automation
- Electroencephalography
- Epilepsies, Partial
- False Positive Reactions
- Humans
- Models, Statistical
- Monitoring, Ambulatory
- ROC Curve
- Reproducibility of Results
- Seizures
- Signal Processing, Computer-Assisted