Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …

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

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by
infrequent seizures based on patient or caregiver reports and limited duration clinical …

Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …

The past twenty-five years have seen a strong increase in the number of publications related
to criticality in different areas of neuroscience. The potential of criticality to explain various …

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

We present and evaluate the capacity of a deep neural network to learn robust features from
EEG to automatically detect seizures. This is a challenging problem because seizure …

Background Seizure prediction would be clinically useful in patients with epilepsy and could
improve safety, increase independence, and allow acute treatment. We did a multicentre …

Background Seizure prediction can increase independence and allow preventative
treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction …

The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …