Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …

The aim of this study is to establish a hybrid model for epileptic seizure detection with
genetic algorithm (GA) and particle swarm optimization (PSO) to determine the optimum …

Epileptic patients suffer from an epileptic brain seizure caused by the temporary and
unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals …

Presently high density EEG systems are available at affordable cost, with which the data
dimension has gone up considerably. For efficient computation of this high-dimensional …

Electroencephalogram (EEG) signal identification based on intelligent models is an
important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional …

The intelligent recognition of electroencephalogram (EEG) signals has become an important
approach to the detection of epilepsy. Among existing intelligent identification methods …

As in many other fields, biomedical engineers benefit from the use of computational
intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even …

Modern NVMe SSDs are widely deployed in diverse domains due to characteristics like high
performance, robustness, and energy efficiency. It has been observed that the impact of …

Computer-aided therapeutics have changed the method of data interpretation for patients
with neurodegenerative disorders. Artificial intelligence (AI)-based clinical practices are not …

This paper aims to optimize the output of diagnosis of the epilepsy activity in EEG (
electroencephalogram) signal by fuzzy logic techniques using genetic algorithms (GA). The …