Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review
Epilepsy is a chronic neurological disorder with a comparatively high prevalence rate. It is a
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
[HTML][HTML] Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals
in the brain, the function of some brain regions is out of balance, leading to the lack of …
in the brain, the function of some brain regions is out of balance, leading to the lack of …
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …
[HTML][HTML] Artificial intelligence in epilepsy
Background: The study of seizure patterns in electroencephalography (EEG) requires
several years of intensive training. In addition, inadequate training and human error may …
several years of intensive training. In addition, inadequate training and human error may …
Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
Objective Automatic detection of epileptic seizures based on deep learning methods
received much attention last year. However, the potential of deep neural networks in seizure …
received much attention last year. However, the potential of deep neural networks in seizure …
Review of challenges associated with the EEG artifact removal methods
Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …
the underlying neuronal activities as electrical signals with high temporal resolution. In …
[HTML][HTML] Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis
Epileptic seizure detection is commonly implemented by expert clinicians with visual
observation of electroencephalography (EEG) signals, which tends to be time consuming …
observation of electroencephalography (EEG) signals, which tends to be time consuming …
Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain
AB Das, MIH Bhuiyan - biomedical signal processing and control, 2016 - Elsevier
In this paper, a comprehensive analysis of focal and non-focal electroencephalography is
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification
J Wang, S Cheng, J Tian, Y Gao - Biomedical Signal Processing and …, 2023 - Elsevier
Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …
into computational information, often used as commands, by recognizing …
A novel robust diagnostic model to detect seizures in electroencephalography
Identifying seizure patterns in complex electroencephalography (EEG) through visual
inspection is often challenging, time-consuming and prone to errors. These problems have …
inspection is often challenging, time-consuming and prone to errors. These problems have …