Posts Tagged seizure forecasting
Epilepsy affects about 50 million people worldwide of which one third is refractory to medication. An automated and reliable system that warns of impending seizures would greatly improve patient’s quality of life by overcoming the uncertainty and helplessness due to the unpredicted events. Here we present new seizure prediction results including a performance comparison of different methods. The analysis is based on a new set of intracranial EEG data that has been recorded in our working group during presurgical evaluation.
We applied two different methods for seizure prediction and evaluated their performance pseudoprospectively. The comparison of this evaluation with common statistical evaluation reveals possible reasons for overly optimistic estimations of the performance of seizure forecasting systems.
Affecting about 1 % of the world population, epilepsy is one of the most common neurological diseases. Although seizures cover relatively short periods in a patient’s life, the uncertainty when the next seizure will occur can produce a high level of anxiety . For 70 % of the patients, medication can reduce the frequency of seizures or even abolish them. However, patients report that unwanted side effects of the medication as well as the unpredictability of seizures are the severest handicaps of this disease . A mobile system with the ability to predict seizures can help to relief the patients’ anxiety related to the uncertainty of events by enabling them to seek shelter, apply a short acting drug or inform the treating physician about the event. The device might also be used to prevent or mitigate the seizure .
Usually, seizure prediction is treated as a binary classification problem of brain activity, recorded as intracranial electroencephalography (icEEG) , with the state of impending seizures (preictal) being labeled as 1 and periods with a big temporal distance to the next seizure (interictal) labeled as 0. In this contribution, we present a new database that has been recorded in our working group. By intensifying the cooperation of clinical research and data analysis we minimize loss of descriptive metadata. For feature extraction and classification of the recorded icEEG signals we employed both, a recently proposed deep convolutional neural network and a featurebased method.