Wendyl Jude D'Souza

The paper uses a mathematical model to analyze EEG data from people with epilepsy and identifies cortisol levels and sleep stages as factors explaining daily variability in epileptiform discharges.

This study analyzes long-term intracranial EEG recordings to examine variability and circadian patterns of high-frequency activity and epileptiform spikes in patients with drug-resistant epilepsy.

This paper presents a deep learning algorithm for computer-assisted EEG review in idiopathic generalized epilepsy, achieving high sensitivity and reducing human review time by 80%-99%.

This study identifies critical slowing as a biomarker for seizure susceptibility using long-term iEEG recordings from epilepsy patients.