Seizure prediction through analysis of the electroencephalogram (EEG) remains a
challenging and thus far unsuccessful task (Mormann et al. 2007). Algorithms employing …

To date, approximately one in four epilepsy patients cannot be treated successfully by
common therapeutic strategies, so they continue to suffer from unforeseen seizures. A …

Seizure prediction performance is hampered by high numbers of false predictions. Here we
present an approach to reduce the number of false predictions based on circadian concepts …

It is now established that epilepsy is characterized by periodic dynamics that increase
seizure likelihood at certain times of day, and which are highly patient-specific. However …

Circadian Profile in Seizure Forecasting amount of relevant high-quality data are provided.
Thus, there is a greater need to understand what pertinent data would feed such systems …

Approximately 30% of epileptic patients are unable to achieve seizure control through
antiepileptic drug administration or surgical procedures. Seizure prediction is a promising …

Figure 1: Seizure Prediction from EEG time series. Continuous multi-channel EEG
recordings are analyzed by means of a moving-window analysis. The data covered by the …

Recent research in the field of seizure prediction has shown that" pre-seizure states" lasting
minutes to hours can be detected from analysis of EEG time series. This time frame would …

A seizure prediction algorithm is proposed that combines novel multivariate EEG features
with patient-specific machine learning. The algorithm computes the eigenspectra of space …

Day and nighttime patterns of seizures have been tied to some of the very first descriptions
of seizures. Giants in the field, such as Fritz Dreifuss, recognized the importance of these …