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.
Epilepsy is a brain disorder that may strike at different stages of life. Patients’ lives are extremely disturbed by the occurrence of sudden unpredictable epileptic seizures. A possible approach to diagnose epileptic patients is to analyze magnetoencephalography (MEG) signals to extract useful information about subject’s brain activities. MEG signals are less distorted than electroencephalogram signals...
The most commonly used clinical tool for initial diagnosis of epilepsy is electroencephalogram (EEG). Recent advances in magnetoencephalography (MEG) technology provide a new source of information to analyze brain activities. In order to determine whether or not particular subjects' brain signals exhibit epileptic activities, epileptologists often spend considerable amount of time to review MEG recordings...
Epilepsy is a brain disorder, where patients’ lives are extremely disturbed by the occurrence of sudden unpredictable seizures. This paper develops patient-independent signal processing techniques based on common spatial patterns and linear discriminant analysis to detect epileptic activities (spikes) from multi-channel brain signal recordings. In contrast to current existing studies which heavily...
One of the key requirement for the development of seizure prediction system is that the seizure alarms generated should be reliable, i.e. the system should have high seizure detection rate and minimum false alarm rate. In this paper, we explore the relationship between the chaotic behavior and energy ratios of the sub bands of an EEG signal. This relationship will then be used to enhance the reliability...
Epilepsy is a brain disorder, which affects around 1% of world population. The life of epilepsy patients can be improved by predicting seizures before its occurrence. It has been observed that EEG signals during the pre-seizure state are less chaotic compared to their behavior at normal state. Therefore, chaoticity measure can be used to develop seizure predictor. In this paper, we propose seizure...
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.