Advances in deep learning methods present new opportunities for fixing complex problems
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …

Identifying seizure activities in non-stationary electroencephalography (EEG) is a
challenging task since it is time-consuming, burdensome, and dependent on expensive …

Using an intelligent method to automatically detect epileptic seizures in medical applications
is one of the most important challenges in recent years to reduce the workload of doctors in …

The ratio of spectral power in two different bands and relative band power have been shown
to be sometimes more discriminative features than the spectral power in a specific band for …

This paper presents novel architectures for machine learning based classifiers using
stochastic logic. Two types of classifier architectures are presented. These include: linear …

Epilepsy can be defined, according to the World Health Organization, as recurrent seizures
related to physical reactions caused by a sudden discharge of electricity to some human …

Background: 1% of people around the world are suffering from epilepsy. It is, therefore
crucial to propose an efficient automated seizure prediction tool implemented in a portable …

Epilepsy is a chronic disorder and outbreak of brain function, caused by the abnormal and
intermittent electric discharge of brain neurons. Electroencephalogram signals represent …

Epilepsy is a form of neurological disorder which results from the immediate change in
neurological signal. Drug-resistant epileptic patients need to be monitored all the time. To …

In this dissertation, we address issues of (a) feature identification and extraction, and (b)
feature selection. Nowadays, datasets are getting larger and larger, especially due to the …