Biomedical signals carry signature rhythms of complex physiological processes that control
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy.
Substantial data is generated by the EEG recordings of ambulatory recording systems, and …

Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving
the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This …

Computer assisted automated detection is highly inevitable for recognizing neurological
disorders, as it involves continuous monitoring of Electroencephalogram (EEG) signal …

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on
electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic …

Epileptic seizure detection classification distinguishes between epileptic and non-epileptic
signals and is an important step that can aid doctors in diagnosing and treating epileptic …

The electroencephalogram (EEG) is the brain signal containing valuable information about
the normal or epileptic state of the brain. In this paper a discrete wavelet-spectral entropy …

This paper suggests the application of gamma band spectral entropy for the detection of
alcoholics. First, the gamma sub band signals (30-50Hz) are extracted using an elliptic band …

Epilepsy is a common brain disorder that about 1% of world's population suffers from this
disorder. EEG signal is summation of brain electrical activities and has a lot of information …

This chapter discusses dynamical neural network architectures for the classification of
medical data. Various researches have indicated that recurrent neural networks such as the …