In machine learning based seizure detection research studies, the number of features
directly affects the performance of models. In order to decrease the amount of features under …

With the increasment of epilepsy patients, traditional epileptic seizure recognition is
generally completed by encephalography (EEG) technicians, which is time-consuming and …

This work plans to propose the novel epileptic seizure detection using the Improved
Ensemble Learning Model (I-ELM). The proposed model focuses five different stages Pre …

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring
electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide …

An automated epilepsy detection method has been proposed by exploiting the multi-domain
features with a few learning algorithms. EEG signals are initially preprocessed to remove the …

Computer-aided seizure detection from the long-term EEG has shown great potential in
improving the epilepsy diagnosis accuracy and efficiency. This study was aimed to utilize …

In this work, we propose a novel feature extraction scheme called multiscale spectral
features (MSSFs) for the design of an automated seizure detection system. The MSSFs are …

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has
been widely used in computer-aided signal analysis of epileptic electroencephalography …

The rapidly rising seizure cases and poor patient-to-neurologist ratio necessitate the
development of an efficient automatic seizure detection system. The most commonly used …

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with
time varying EEG signals is an essential procedure in intensive care units. There is an …