Epileptic seizure detection

Seizures are the phenomenon of rhythmic discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are non-stationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Selected segments of the EEG signals are analyzed using time-frequency methods and the features are extracted for each segment. These features are used as an input to the artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. Also, this paper presents another method of analysis of EEG signals based on K-means nearest neighbor classifier and the performance of this classifier is tested on a prior labeled EEG database consisting of normal and epileptic samples. The performance indicates overall accuracy from 84% to 98%.