Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach

Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected by clinicians based on visual observation of EEG recordings, which is generally time consuming and sensitive to bias. This paper presents a novel automatic seizure detection method based on the multiscale radial basis function (MRBF) networks and the Fisher vector (FV) encoding. Specifically, the MRBF networks are first used to obtain high-resolution time-frequency (TF) images for feature extraction, where both a modified particle swarm optimization (MPSO) method and an orthogonal least squares (OLS) algorithm are implemented to determine optimal scales and detect a parsimonious model structure. Gray level co-occurrence matrix (GLCM) texture descriptors and the FV, which contribute to high-dimensional vectors, are then adopted to achieve discriminative features based on five frequency subbands of clinical interests from TF images. Furthermore, the dimensionality of the original feature space can be effectively reduced by the t-test statistical tool before feeding compact features into the SVM classifier for seizure detection. Finally, the classification performance of the proposed method is evaluated by using two widely used EEG database, and is observed to provide good classification accuracy on both datasets. Experimental results demonstrate that our proposed method is a powerful tool in detecting epileptic seizures