Context-learning based electroencephalogram analysis for epileptic seizure detection

Epileptic seizure is a serious health problem in the world and there is a huge population suffering from it every year. If an algorithm could automatically detect seizures and deliver the patient therapy or notify the hospital, that would be of great assistance. Analyzing the scalp EEG is the most common way to detect the onset of a seizure. In this paper, we proposed the context-learning based EEG analysis for seizure detection (Context-EEG) algorithm. The proposed method aims at extracting both the hidden inherent features within EEG fragments and the temporal features from EEG contexts. First, we segment the EEG signals into EEG fragments of fixed length. Second, we learn the hidden inherent features from each fragment and reduce the dimensionality of the original data. Third, we translate each EEG fragment to an EEG word so that the EEG context can provide us with temporal information. And finally, we concatenate the hidden feature and the temporal feature together to train a binary classifier. The experiment result shows the proposed model is highly effective in detecting seizure.