Cycles in epilepsy
Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that
seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans …
seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans …
[HTML][HTML] 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 …
Forecasting seizure risk in adults with focal epilepsy: a development and validation study
Background People with epilepsy are burdened with the apparent unpredictability of
seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) …
seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) …
Machine learning and wearable devices of the future
Abstract Machine learning (ML) is increasingly recognized as a useful tool in healthcare
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
[HTML][HTML] 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 forecasting and cyclic control of seizures
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where
causes, underlying biomarkers, triggers, and patterns differ across individuals. The …
causes, underlying biomarkers, triggers, and patterns differ across individuals. The …
Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree
Electroencephalography is frequently used for diagnostic evaluation of various brain-related
disorders due to its excellent resolution, non-invasive nature and low cost. However, manual …
disorders due to its excellent resolution, non-invasive nature and low cost. However, manual …
Energy-efficient neural network for epileptic seizure prediction
Seizure prediction for drug-refractory epilepsy patients can improve their quality of life,
reduce their anxiety, and help them take the necessary precautions. Nowadays, numerous …
reduce their anxiety, and help them take the necessary precautions. Nowadays, numerous …
Semi-dilated convolutional neural networks for epileptic seizure prediction
Epilepsy is a neurological brain disorder that affects∼ 75 million people worldwide.
Predicting epileptic seizures holds great potential for improving the quality of life of people …
Predicting epileptic seizures holds great potential for improving the quality of life of people …
A deep fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power
P Peng, L Xie, H Wei - International journal of neural systems, 2021 - World Scientific
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …
resistant epilepsy. Conventional methods usually adopt handcrafted features and manual …