In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band
specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is …

Feature selection methods can be applied to enhance the EEG classification more
accurately along with its applicability for the large volume of data. Univariate filter feature …

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to
identify epileptic seizures, which leads to heavy workloads and is time consuming. However …

The epileptic seizure detection and classification is of great significance for clinical
diagnosis and treatment. To realize the detection and classification of epileptic seizure, this …

Purpose This work proposes a tertiary wavelet model based automatic epilepsy
classification system using electroencephalogram (EEG) signals. Design/methodology …

EEG (Electroencephalogram) is a technique for identifying neurological disorders. There are
various neurological disorders like Epilepsy, brain cancer, etc. Feature extraction and …

Most research to date using hybrid systems (Fuzzy-Neuro) focused on the Multi-Layer
Perceptron (MLP). Alternative neural network approaches such as the Radial Basis Function …

In this study, it has been intended to analyze Electroencephalography (EEG) signals by
Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural …

The electroencephalograph (EEG) signal is one of the most widely used signals in the
biomedicine field due to its rich information about human tasks. This research study …

Epilepsy is a persistent neurological condition that impacts a significant portion of the
worldwide population. Accurate identification is necessary in order to efficiently diagnose …