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

Electroencephalography (EEG) is the signal of intrigue that has immense application in the
clinical diagnosis of various neurological, psychiatric, psychological, psychophysiological …

Highlights:► Twelve entropy indices were systematically compared in monitoring depth of
anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in …

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They
are highly random in nature and may contain useful information about the brain state …

Fractional processes are widely found in science, technology and engineering systems. In
Fractional Processes and Fractional-order Signal Processing, some complex random …

A wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha,
beta, and gamma subbands of EEGs for detection of seizure and epilepsy. The nonlinear …

Achieving the goal of detecting seizure activity automatically using electroencephalogram
(EEG) signals is of great importance and significance for the treatment of epileptic seizures …

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures.
Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are …

Deep learning (DL) methods have been widely used in the field of seizure prediction from
electroencephalogram (EEG) in recent years. However, DL methods usually have numerous …

Epilepsy is a common neurological disorder that is characterized by the recurrence of
seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures …