D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism

AA Khan, RK Madendran… - CAAI Transactions …, 2023 - Wiley Online Library
Epilepsy is considered as a serious brain disorder in which patients frequently experience
seizures. The seizures are defined as the unexpected electrical changes in brain neural …

Enhancing performance of convolutional neural network-based epileptic electroencephalogram diagnosis by asymmetric stochastic resonance

Z Shi, Z Liao, H Tabata - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is
clinically diagnosed primarily by electroencephalography. Several intelligent systems have …

Machine learning based classification of EEG signal for detection of child epileptic seizure without snipping

PK Sethy, M Panigrahi, K Vijayakumar… - International Journal of …, 2023 - Springer
The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-
term EEG recordings of an epileptic patient contain a huge amount of EEG data. Therefore …

Efficient seizure prediction and EEG channel selection based on multi-objective optimization

R Jana, I Mukherjee - IEEE Access, 2023 - ieeexplore.ieee.org
Epileptic seizures are unpredictable events due to sudden abnormal electrical activities in
the brain of epilepsy patients. A seizure can be predicted by analyzing the EEG signals to …

An epileptic seizures diagnosis system using feature selection, fuzzy temporal naive Bayes and T-CNN

P Srihari, V Santosh, S Ganapathy - Multimedia Tools and Applications, 2023 - Springer
Today's hospitals make use of state-of-the-art methods such as magnetic resonance
imaging (MRI) and electroencephalogram (EEG) signal predictions in order to predict the …

A self-attention model for cross-subject seizure detection

T Abdallah, N Jrad, F Abdallah… - Computers in Biology …, 2023 - Elsevier
Epilepsy is a neurological disorder characterized by recurring seizures, detected by
electroencephalography (EEG). EEG signals can be detected by manual time-consuming …

Improved patient-independent seizure detection using hybrid feature extraction approach with atomic function-based wavelets

D Nandini, J Yadav, A Rani, V Singh… - Iranian Journal of …, 2023 - Springer
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 …

A self-interpretable deep learning model for seizure prediction using a multi-scale prototypical part network

Y Gao, A Liu, L Wang, R Qian… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of
seizures, which is crucial to improving patients' quality of life. Many deep learning-based …

[HTML][HTML] A novel epilepsy seizure prediction model using deep learning and classification

B Jaishankar, AM Ashwini, D Vidyabharathi, L Raja - Healthcare Analytics, 2023 - Elsevier
Epilepsy is a common neurological disease where the earlier disease prediction
significantly impacts those patients' lives. In this paper, a novel epilepsy seizure prediction …

[PDF][PDF] Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI.

F Mohammad, S Al-Ahmadi - Computers, Materials & Continua, 2023 - researchgate.net
There exists various neurological disorder based diseases like tumor, sleep disorder,
headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological …