title: Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis.
abstract: We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.
authors: Department of Signals and Systems, School of Electrical Engineering, University of Belgrade Belgrade, Serbia ; Center of Excellence DEWS, University of L'Aquila L'Aquila, Italy.
authors: Dragoljub
authors: D
authors: Gajic
authors: Department of Signals and Systems, School of Electrical Engineering, University of Belgrade Belgrade, Serbia.
authors: Zeljko
authors: Z
authors: Djurovic
authors: Faculty of Engineering, University of Kragujevac Kragujevac, Serbia.
authors: Jovan
authors: J
authors: Gligorijevic
authors: Center of Excellence DEWS, University of L'Aquila L'Aquila, Italy.
authors: Stefano
authors: S
authors: Di Gennaro
authors: Faculty of Technology, University of Nis Leskovac, Serbia.
authors: Ivana
authors: I
authors: Savic-Gajic
keywords: epileptiform activity; non-linear analysis; quadratic classifiers; scatter matrices; seizure detection