Deep learning for the automated detection of epileptic seizures has received much attention
during recent years. In this work, one dimensional convolutional neural network (1D-CNN) …

In this paper, seizure activities of EEG signals have been detected exploiting 1-D
convolutional neural network architecture of deep learning algorithm. In past some years …

This work aims to develop an end-to-end solution for seizure onset detection. We design the
SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with …

The manual review of an electroencephalogram (EEG) for seizure detection is a laborious
and error-prone process. Thus, automated seizure detection based on machine learning has …

Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …

The use of feature extraction and selection from EEG signals has shown to be useful in the
detection of epileptic seizure segments. However, these traditional methods have more …

Background Automated seizure detection from clinical EEG data can reduce the diagnosis
time and facilitate targeting treatment for epileptic patients. However, current detection …

Comparison of Different Input Modalities and Network Structures for Deep Learning-Based
Seizure Detection Cho KO, Jang HJ. Sci Rep. 2020; 10 (1): 1-11. doi: 10.1038/s41598-019 …

An epileptic seizure, a disorder in brain functionality, happens when electrical bursts spread
across the brain, causing the person to lose control or consciousness. Predicting epileptic …

Neural networks and deep learning has gained much attention in the recent years in the
medical field. Recent improvements in deep learning has led to computer-aided analysis of …