EEG seizure detection: concepts, techniques, challenges, and future trends
AA Ein Shoka, MM Dessouky, A El-Sayed… - Multimedia Tools and …, 2023 - Springer
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
Epilepsy detection from EEG using complex network techniques: A review
Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-
third of epileptic patients experience seizures attack even with medicated treatment. The …
third of epileptic patients experience seizures attack even with medicated treatment. The …
Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection
K Akyol - Expert Systems with Applications, 2020 - Elsevier
Electroencephalography signals obtained from the brain's electrical activity are commonly
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
M Varlı, H Yılmaz - Journal of Computational Science, 2023 - Elsevier
Epilepsy stands out as one of the common neurological diseases. The neural activity of the
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Time-frequency domain deep convolutional neural network for the classification of focal and non-focal EEG signals
S Madhavan, RK Tripathy, RB Pachori - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
The neurological disease such as the epilepsy is diagnosed using the analysis of
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
Hierarchical Harris hawks optimization for epileptic seizure classification
Z Luo, S Jin, Z Li, H Huang, L Xiao, H Chen… - Computers in Biology …, 2022 - Elsevier
The intelligent recognition of electroencephalogram (EEG) signals is a valuable tool for
epileptic seizure classification. Given that visual inspection of EEG signals is time …
epileptic seizure classification. Given that visual inspection of EEG signals is time …
Epileptic seizure detection with EEG textural features and imbalanced classification based on EasyEnsemble learning
Imbalance data classification is a challenging task in automatic seizure detection from
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …
[HTML][HTML] Epileptic seizure detection using cross-bispectrum of electroencephalogram signal
Purpose The automatic detection of epileptic seizures in EEG data from extended recordings
can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce …
can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce …
Machine learning algorithms for epilepsy detection based on published eeg databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …