With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

The sudden and apparently unpredictable nature of seizures is one of the most disabling
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …

Research in seizure prediction has come a long way since its debut almost 4 decades ago.
Early studies suffered methodological caveats leading to overoptimistic results and lack of …

This paper presents a patient‐specific epileptic seizure predication method relying on the
common spatial pattern‐(CSP‐) based feature extraction of scalp electroencephalogram …

A novel patient-specific seizure prediction method based on the analysis of positive zero-
crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window …

Epilepsy is one of the most common brain disorders, but the dynamical transitions to
neurological dysfunctions of epilepsy are not well understood in current neuroscience …

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the
world's population. In this neurological disorder, abnormal activity of the brain causes …

Statistical methods for evaluating seizure prediction algorithms are controversial and a
primary barrier to realizing clinical applications. Experts agree that these algorithms must, at …

A robust seizure prediction methodology would enable a “closed-loop” system that would
only activate as impending seizure activity is detected. Such a system would eliminate …

OBJECTIVE: Identification of consistent distinguishing features between preictal and
interictal periods in the EEG is an essential step towards performing seizure prediction. We …