EMD Analysis of EEG Signals for Seizure Detection

The Electroencephalogram (EEG) is the electrical signals which contain the information related to activities within the brain. Neurological disorders such as epilepsy can be diagnosed effectively by analyzing EEG signals. In the present work, the empirical mode decomposition (EMD) is applied to EEG recordings for the automated detection of seizures in epileptic patients. For this purpose, intrinsic mode functions (IMFs) from the EMD are processed to extract the features from normal and seizure EEG signals. The extracted features are ranked to select the useful features for classification. The classification was done by using these selected features by Artificial Neural Network (ANN). The EEG dataset used in the present study is the well-known publicly available Bonn University EEG dataset. Three different classification problems were done by using the sets of this data. The classification accuracy achieved for these three cases were 96.1, 96.4, and 99.3%.