Automatic seizure detection: going from sEEG to iEEG
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Automatic seizure detection: going from sEEG to iEEG. / Duun-Henriksen, Jonas; Remvig, Line S; Madsen, Rasmus Elsborg; Conradsen, Isa; Kjaer, Troels W; Thomsen, Carsten E; Sorensen, Helge B D.
In: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Vol. 2010, 31.08.2010, p. 2431-4.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Automatic seizure detection: going from sEEG to iEEG
AU - Duun-Henriksen, Jonas
AU - Remvig, Line S
AU - Madsen, Rasmus Elsborg
AU - Conradsen, Isa
AU - Kjaer, Troels W
AU - Thomsen, Carsten E
AU - Sorensen, Helge B D
PY - 2010/8/31
Y1 - 2010/8/31
N2 - 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.
AB - 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.
KW - Algorithms
KW - Automatic Data Processing
KW - Automation
KW - Electroencephalography
KW - Epilepsies, Partial
KW - False Positive Reactions
KW - Humans
KW - Models, Statistical
KW - Monitoring, Ambulatory
KW - ROC Curve
KW - Reproducibility of Results
KW - Seizures
KW - Signal Processing, Computer-Assisted
U2 - 10.1109/IEMBS.2010.5626305
DO - 10.1109/IEMBS.2010.5626305
M3 - Journal article
C2 - 21095958
VL - 2010
SP - 2431
EP - 2434
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SN - 0589-1019
Y2 - 31 August 2010 through 4 September 2010
ER -
ID: 33900827