Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

LA Al-Haddad, WH Alawee, A Basem - Computers in Biology and Medicine, 2024 - Elsevier
In the rapidly advancing field of biomedical engineering, effective real-time control of
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …

Residual and bidirectional LSTM for epileptic seizure detection

W Zhao, WF Wang, LM Patnaik, BC Zhang… - Frontiers in …, 2024 - frontiersin.org
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic
seizures, which affects over 70 million people in the world. Nonetheless, the visual …

Multiband seizure type classification based on 3D convolution with attention mechanisms

H Huang, P Chen, J Wen, X Lu, N Zhang - Computers in Biology and …, 2023 - Elsevier
Electroencephalogram (EEG) signal contains important information about abnormal brain
activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy …

EEG-based epileptic seizure detection using deep learning techniques: A survey

J Xu, K Yan, Z Deng, Y Yang, JX Liu, J Wang, S Yuan - Neurocomputing, 2024 - Elsevier
Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming
from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and …

Using Explainable Artificial Intelligence to obtain efficient seizure-detection models based on electroencephalography signals

JC Vieira, LA Guedes, MR Santos, I Sanchez-Gendriz - Sensors, 2023 - mdpi.com
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their
quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of …

Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs

C Liu, W Chen, T Zhang - Biocybernetics and Biomedical Engineering, 2023 - Elsevier
Wavelet based seizure detection is an importance topic for epilepsy diagnosis via
electroencephalogram (EEG), but its performance is closely related to the choice of wavelet …

Spatiotemporal analysis of interictal EEG for automated seizure detection and classification

RK Joshi, V Kumar, M Agrawal, A Rao, L Mohan… - … Signal Processing and …, 2023 - Elsevier
Objective Seizure type classification is important as therapy differs for different epilepsy
subtypes. Currently, skilled neurologists classify seizures based on visual analysis …

Automated multi-class seizure-type classification system using EEG signals and machine learning algorithms

S Abirami, M Kathiravan, R Yuvraj, RN Menon… - IEEE …, 2024 - ieeexplore.ieee.org
Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. The
treatment for epilepsy is influenced by the types of seizures. Therefore, develo** a …

[HTML][HTML] Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM

KK Palanisamy, A Rengaraj - Brain Sciences, 2024 - mdpi.com
In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic
seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging …

Detection of epileptic seizure events using pre‐trained convolutional neural network, VGGNet and ResNet

DK Thara, BG Premasudha, S Krivic - Expert Systems, 2023 - Wiley Online Library
Epilepsy is a life threatening neurological disorder. The person with epilepsy suffers from
recurrent seizures. Sudden emission of electrical signal in the nerves of the human brain is …