Electroencephalogram (EEG) is a record of ongoing electrical signal to represent the human brain activity. It has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classiÃ‚Â¯cation is a crucial task to detect the stage of ictal (i.e. seizure period) and interictal (i.e. periodbetween seizures) EEG signals for the treatment and precaution of the patient. However, existing seizure and nonseizure feature extraction techniques are not good enough for the classiÃ‚Â¯cation of ictal and interictal EEG signals considering their non-abrupt phenomena and inconsistency in diÃ‚Â®erent brain locations. In this paper, we present newapproaches for feature extraction using high-frequency components from discrete cosine transformation (DCT) and intrinsic mode function (IMF) extracted from empirical mode decomposition (EMD). These features are then used as an input to least square-support vector machine (LV-SVM) to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classiÃ‚Â¯cation in terms of sensitivity, speciÃ‚Â¯city, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark dataset from diÃ‚Â®erent brain locations.
|Number of pages||9|
|Journal||Biomedical Engineering - Applications, Basis and Communications|
|Publication status||Published - Jun 2015|