Health Electroencephalogram epileptic classification based on Hilbert probability similarity

This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. probability-based measure is exploited the between The system consisted of models (HPS) predict state for specific EEG signal. Particle swarm optimization (PSO) been employed feature selection and extraction. Furthermore, used dataset in this study Bonn University's publicly available dataset. Several metrics are calculated assess performance suggested systems such as accuracy, precision, recall, F1-score. experimental results show that model an effective tool classifying signals, with accuracy up 100% two-class status.