A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …

Around a third of the total population of Europe suffers from mental disorders. The use of
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …

Visual inspection of long-term electroencephalography (EEG) is a tedious task for
physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural …

The automatic detection of epileptic seizures by Electroencephalogram (EEG) can
accelerate the diagnosis of the disease by neurologists, which is of incredible importance for …

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and
improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly …

Due to the lack of multiscale feature extraction and bidirectional feature learning abilities, the
existing deep state-of-charge (SOC) estimators are difficult to capture:(1) localized invariant …

Epilepsy is a neurological disorder caused by brain dysfunction, which could cause
uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography …

Epilepsy is one of the most common neurological disorders typically characterized by
recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy …

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …

Stress has an impact not only on a person's physical health but also on his or her ability to
perform at work, passion, and attitude in day-to-day life. It is one of the most difficult …