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 …

This study describes a generalized cross‐patient seizure‐forecasting approach using
recurrent neural networks with ultra‐long‐term subcutaneous EEG (sqEEG) recordings …

The ability to forecast seizures minutes to hours in advance of an event has been verified
using invasive EEG devices, but has not been previously demonstrated using noninvasive …

Accurate identification of seizure activity, both clinical and subclinical, has important
implications in the management of epilepsy. Accurate recognition of seizure activity is …

Objective. This paper introduces a fully automated, subject-specific deep-learning
convolutional neural network (CNN) system for forecasting seizures using ambulatory …

The unpredictability of epileptic seizures exposes people with epilepsy to potential physical
harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure …

Objective Seizure forecasting may provide patients with timely warnings to adapt their daily
activities and help clinicians deliver more objective, personalized treatments. Although …

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

It is now established that epilepsy is characterized by periodic dynamics that increase
seizure likelihood at certain times of day, and which are highly patient-specific. However …

Objective Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective
outpatient recording of electrographic seizures as an alternative to self-reported epileptic …