The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The automatic seizure prediction technology is crucial to develop a new therapy for the patients suffering from medically intractable epilepsy. This paper proposes an efficient and low-complexity method for seizure prediction. The univariate feature of line length can describe both amplitude and frequency variations of an EEG signal, and the bivariate feature of mean phase coherence (MPC) is able...
The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm.The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.