Using sequential floating forward selection algorithm to detect epileptic seizure in EEG signals

Epilepsy is a common neurological disorder involving spontaneous seizures. While electroencephalography (EEG) is a useful diagnostic approach of epilepsy that records the electrical activity of the brain, detection of epileptic seizures is still clinically difficult. This study proposes a segmental classification approach for the detection of epileptic seizure in EEG signals. Regularized least squares and smoothness priors methods are applied to minimize the nonstationary components in the signals. The optimal frequency band energy features are selected by using the sequential floating forward selectrion (SFFS) algorithm, with linear, quadratic and cubic discriminant function as classifiers. The results show that when the quadratic discriminant function is applied, the sensitivity and specificity of seizure detection reach a maximum of 98.1% and 95.6% respectively for discriminating health subjects against epileptic subjects in seizure period, and the overall classification rate is 97.2%.