Epileptic seizure detection in EEG signals using normalized IMFs in CEEMDAN domain and quadratic discriminant classifier

Epilepsy is the fourth most common neurological disorder that manifests itself through unprovoked seizures, detection of which is the very first step of proper diagnosis and treatment of this severe disease. In this paper, an automated seizure detection method has been proposed based on the statistical and spectral features of max normalized intrinsic mode functions or IMFs that were extracted using complete ensemble empirical mode decomposition with adaptive noise method. First, a publicly available dataset of EEG signals was used to generate the IMFs and noise or outliers were discarded. Then IMFs were max normalized which was shown to improve the separability of features. Statistical and spectral features were extracted from the normalized IMFs which offered better separation of seizure and seizure-free data. Finally, Quadratic Discriminant classifier was used for the classification purpose and 10-fold cross validation was performed to validate the trained model. The proposed scheme is numerically efficient and shows a maximum of 100% accuracy which is the highest reported on this data set.