Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

Artificial intelligence has the potential to revolutionize modern society in all its aspects.
Encouraged by the variety and vast amount of data that can be gathered from patients (eg …

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …

The detection of seizures in epileptic patients via Electroencephalography (EEG) is an
essential key to medical treatment. With the advances in deep learning, many approaches …

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 …

Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model,
which cannot extract local and global features simultaneously. To this end, we propose an …

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 …

Epileptic seizures can affect the patient's physical function and cause irreversible damage to
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …

A seizure is an unstable situation in epilepsy patients due to excessive electrical discharge
by brain cells. An efficient seizure prediction method is required to reduce the lifetime risk of …