Objective There are no validated methods for predicting the timing of seizures. Using
machine learning, we sought to forecast 24‐hour risk of self‐reported seizure from e‐diaries …

Many outstanding studies have reported promising results in seizure forecasting, one of the
most challenging predictive data analysis problems. This is mainly because …

Many recent studies on online seizure prediction from iEEG signal describe various
prediction algorithms and their prediction performance. In contrast, this paper focuses on …

This review highlights recent developments in the field of epileptic seizure prediction. We
argue that seizure prediction is possible; however, most previous attempts have used data …

Background People with epilepsy are burdened with the apparent unpredictability of
seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) …

A lot of mileage has been made recently on the long and winding road toward seizure
forecasting. Here we briefly review some selected milestones passed along the way, which …

Today's modalities for short-term monitoring of EEG are primarily meant for supporting
clinical diagnosis of epilepsy or classifying seizures and interictal epileptiform discharges …

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus
suboptimal quality in epilepsy management. The limitations of existing hospital‐and home …

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 …