Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting
C Meisel, R El Atrache, M Jackson, S Schubach… - …, 2020 - Wiley Online Library
Objective Seizure forecasting may provide patients with timely warnings to adapt their daily
activities and help clinicians deliver more objective, personalized treatments. Although …
activities and help clinicians deliver more objective, personalized treatments. Although …
Forecasting seizure likelihood with wearable technology
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 …
harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure …
Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
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 …
using invasive EEG devices, but has not been previously demonstrated using noninvasive …
Seizure detection using wearable sensors and machine learning: Setting a benchmark
Objective Tracking seizures is crucial for epilepsy monitoring and treatment evaluation.
Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may …
Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may …
Artificial intelligence‐enhanced epileptic seizure detection by wearables
Objective Wrist‐or ankle‐worn devices are less intrusive than the widely used
electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom …
electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom …
Seizure diaries and forecasting with wearables: epilepsy monitoring outside the clinic
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 …
infrequent seizures based on patient or caregiver reports and limited duration clinical …
Epileptic seizure prediction using big data and deep learning: toward a mobile system
Background Seizure prediction can increase independence and allow preventative
treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction …
treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction …
Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models
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 …
unpredictability of seizures. Cumulative research in the past decades has advanced our …
Non-invasive wearable seizure detection using long–short-term memory networks with transfer learning
Objective. The detection of seizures using wearable devices would improve epilepsy
management, but reliable detection of seizures in an ambulatory environment remains …
management, but reliable detection of seizures in an ambulatory environment remains …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …