Automatic seizure detection: going from sEEG to iEEG

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

SN - 0589-1019

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

T2 - Annual International Conference of the IEEE 2010

Y2 - 31 August 2010 through 4 September 2010

ER -