TY - GEN
T1 - Multichannel-based newborn eeg seizure detection using time-frequency matched filter
AU - Khlif, M. S.
AU - Mesbah, M.
AU - Boashash, B.
AU - Colditz, P.
PY - 2007
Y1 - 2007
N2 - In recent years, much effort has been made toward developing computerized methods to detect seizures. In adults, the clinical signs of seizures are well defined and easily recognizable. But in newborns, these signs are either subtle or completely absent. For this reason, the electroencephalogram (EEG) has been the most dependable tool used for detecting seizures in newborns. Considering the non-stationary and multicomponent nature of the EEG signals, timefrequency (TF) based methods were found to be very suitable for the analysis of such signals. Using TF representation of EEG signals allows extracting TF signatures that are characteristic of EEG seizures. In this paper we present a TF method for newborn EEG seizure detection using a TF matched filter. The threshold used to distinguish between seizure and nonseizure is data-dependent and is set using the EEG background. Multichannel geometrical correlation, based on a concept of incidence matrix, was utilized to further enhance the performance of the detector.
AB - In recent years, much effort has been made toward developing computerized methods to detect seizures. In adults, the clinical signs of seizures are well defined and easily recognizable. But in newborns, these signs are either subtle or completely absent. For this reason, the electroencephalogram (EEG) has been the most dependable tool used for detecting seizures in newborns. Considering the non-stationary and multicomponent nature of the EEG signals, timefrequency (TF) based methods were found to be very suitable for the analysis of such signals. Using TF representation of EEG signals allows extracting TF signatures that are characteristic of EEG seizures. In this paper we present a TF method for newborn EEG seizure detection using a TF matched filter. The threshold used to distinguish between seizure and nonseizure is data-dependent and is set using the EEG background. Multichannel geometrical correlation, based on a concept of incidence matrix, was utilized to further enhance the performance of the detector.
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U2 - 10.1109/IEMBS.2007.4352527
DO - 10.1109/IEMBS.2007.4352527
M3 - Conference contribution
C2 - 18002193
AN - SCOPUS:57649158808
SN - 1424407885
SN - 9781424407880
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 1265
EP - 1268
BT - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
T2 - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Y2 - 23 August 2007 through 26 August 2007
ER -