Enhance Feature Representation Of Electroencephalogram For Seizure Detection

In the treatment of epilepsy with intracranial electroencephalogram(iEEG), the recognition accuracy is low, and it is difficult to find the correlation between channels because of the large amount of channel numbers and time series data. In order to solve these problems, we propose a novel EEG feature prepresentation method for seizure detection based on the Log Mel-Filterbank energy feature. We propose to adapt the Mel-Filterbank energy to EEG features with logrithm transform in the frequency domain. Meanwhile, we also propose the sequential forward channel selection(SFCS) algorithm to incorporate channel correlation and balance the computing consumption. Experiments show that our proposed method have significant contributions in channel selection and feature representation to the problem of EEG signal analysis. The average results of experiments judged by the mean area under the ROC curve (AUC) of the probability reach 99.13%.