Seizure detection in EEG signals: a comparison of different approaches.

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression,discrete wavelet transform and time frequency distributions.We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database

Detection of rhythmic discharges in newborn EEG signals.

This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed

Detection of Rhythmic Discharges in Newborn EEG Signals

This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed

Detection of rhythmic discharges in newborn EEG signals.

This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed