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 …

Epilepsy detection from EEG using complex network techniques: A review

S Supriya, S Siuly, H Wang… - IEEE Reviews in …, 2021 - ieeexplore.ieee.org
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 …

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 …

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 …

Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features

A Malekzadeh, A Zare, M Yaghoobi, HR Kobravi… - Sensors, 2021 - mdpi.com
Epilepsy is a brain disorder disease that affects people's quality of life.
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 …

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 detection with EEG textural features and imbalanced classification based on EasyEnsemble learning

C Sun, H Cui, W Zhou, W Nie, X Wang… - International journal of …, 2019 - World Scientific
Imbalance data classification is a challenging task in automatic seizure detection from
electroencephalogram (EEG) recordings when the durations of non-seizure periods are …

[HTML][HTML] Epileptic seizure detection using cross-bispectrum of electroencephalogram signal

N Mahmoodian, A Boese, M Friebe, J Haddadnia - seizure, 2019 - Elsevier
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 …

Machine learning algorithms for epilepsy detection based on published eeg databases: A systematic review

A Miltiadous, KD Tzimourta, N Giannakeas… - IEEE …, 2022 - ieeexplore.ieee.org
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …