Seizure prediction for untreatable epileptic patients, one of the major challenges of present neuroinformatics researchers, will allow a substantial improvement in their safety and quality of life. Neural networks, because of their plasticity and degrees of freedom, seem to be a good approach to consider the enormous variability of physiological systems. Several architectures and training algor...

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done u...

Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will interfere with patient’s normal behavior consciousness, having great impact on their life. The purpose of this study was to design seizure prediction model improve the quality patients’ lives assist doctors in making diagnostic decisions. This paper presents transformer-based model. Firstly, time-frequ...

If seizure forecasting systems could reliably identify periods of increased probability of seizure occurrence, patients who suffer from epilepsy would be able to avoid dangerous activities and lead more normal lives. The goal of this project is to differentiate between the preictal and interictal states by analyzing intracranial EEG recordings. Data for each hour are organized into six ten-minu...