This project uses EEG data to detect epileptic seizures with machine learning models, focusing on CNN and RNN architectures. It includes preprocessing, feature extraction, and model …

Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of …

... discussed approaches can be summarized using the automatic seizure detection flow diagram shown in Figure 1, which includes the typical stages of EEG data preprocessing, feature...

Jun 1, 2021 · This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals.

Jan 19, 2024 · In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI …

The EEG signals that are contained by PCI-MIO 16E DAQ card system that provides real time processing and is a data bus of computer, signal processor and personal computer.

By this methodology, the raw EEG signal is transformed to its graph representation, and a GNN model is used to learn latent features and classify whether the data indicates an epileptic …

Main Result: The proposed method was tested with read EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and...

In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods …

Researchers have proposed methods for the detection of seizures using features extracted from EEG signals by hand-engineered techniques. Some of the proposed methods use spectral …