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

Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …

To date, the unpredictability of seizures remains a source of suffering for people with
epilepsy, motivating decades of research into methods to forecast seizures. Originally, only …

Objective One of the most disabling aspects of living with chronic epilepsy is the
unpredictability of seizures. Cumulative research in the past decades has advanced our …

Objective. To perform automatic sleep scoring based only on intracranial
electroencephalography (iEEG), without the need for scalp EEG), electrooculography (EOG) …

Objective. Electrical deep brain stimulation (DBS) is an established treatment for patients
with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS …

Objective Ultra long‐term subcutaneous electroencephalography (sqEEG) monitoring is a
new modality with great potential for both health and disease, including epileptic seizure …

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-
long-term electroencephalography (EEG) recordings. Given the diversity of devices …

Human sleep can be described as a series of transitions between distinct states. This makes
automatic sleep analysis (scoring) suitable for an automatic implementation using machine …

Background Epileptic seizures are spontaneous events that severely affect the lives of
patients due to their recurrence and unpredictability. The integration of new wearable and …