A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
Structure‐Based Drug Discovery with Deep Learning
Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and
chemical biology, for example, to predict protein structure and molecular bioactivity, plan …
chemical biology, for example, to predict protein structure and molecular bioactivity, plan …
Automatic seizure detection based on imaged-EEG signals through fully convolutional networks
Seizure detection is a routine process in epilepsy units requiring manual intervention of well-
trained specialists. This process could be extensive, inefficient and time-consuming …
trained specialists. This process could be extensive, inefficient and time-consuming …
SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals
Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions,
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
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 …
Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
S Saminu, G Xu, S Zhang… - Artificial Intelligence …, 2023 - ojs.bonviewpress.com
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …
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 …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
Design possibilities and challenges of DNN models: a review on the perspective of end devices
Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …
resource-constrained devices have become abundant in recent years. As of now, the …
XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series
In clinical practice, algorithmic predictions may seriously jeopardise patients' health and thus
are required to be validated by medical experts before a final clinical decision is met …
are required to be validated by medical experts before a final clinical decision is met …