In this paper, a comprehensive analysis of focal and non-focal electroencephalography is
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …

In this paper, we address the problem of off-line supervised detection of epileptic seizures in
long-term Electroencephalography (EEG) records. A novel feature extraction method is …

Epileptic neurological disorder of the brain is widely diagnosed using the
electroencephalography (EEG) technique. EEG signals are nonstationary in nature and …

Approximately one third of all epilepsy patients are resistant to current therapeutic
treatments. Some patients with focal forms of epilepsy benefit from invasive surgical …

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure
activity and improving patients' quality of life. Accurate evaluation, presurgical assessment …

This paper proposes a patient-specific supervised classification algorithm to detect seizures
in long offline intracranial electroencephalographic (iEEG) recordings. The main idea of the …

This work presents a new method that combines symbol dynamics methodologies with an
Ngram algorithm for the detection and prediction of epileptic seizures. The presented …

Objective In long-term video-monitoring, automatic seizure detection holds great promise as
a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) …

Today, localization of the seizure focus heavily relies on EEG monitoring (scalp or
intracranial). However, current technology enables much finer resolutions. The activity of …

High-frequency (HF) activity represents a potential biomarker of the epileptogenic zone in
epilepsy patients, the removal of which is considered to be crucial for seizure-free surgical …