[Retracted] EEG‐Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
I Ahmad, X Wang, M Zhu, C Wang, Y Pi… - Computational …, 2022 - Wiley Online Library
Epileptic seizure is one of the most chronic neurological diseases that instantaneously
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …
[HTML][HTML] Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
[HTML][HTML] A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
Machine learning for predicting epileptic seizures using EEG signals: A review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …
researchers are striving towards employing these techniques for advancing clinical practice …
[Retracted] Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health.
It occurs abruptly without any symptoms and thus increases the mortality rate of humans …
It occurs abruptly without any symptoms and thus increases the mortality rate of humans …
[HTML][HTML] Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer's disease
Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease
(AD) commonly leads to improved performance than those using a single modality …
(AD) commonly leads to improved performance than those using a single modality …
Spatio-temporal MLP network for seizure prediction using EEG signals
In this paper, we propose an end-to-end epilepsy seizure prediction method based on multi-
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
layer perceptrons (MLPs). The proposed method mainly contains two functional blocks: the …
Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier
The electroencephalogram (EEG) signal contains useful information on physiological states
of the brain and has proven to be a potential biomarker to realize the complex dynamic …
of the brain and has proven to be a potential biomarker to realize the complex dynamic …
EEG-based seizure prediction via hybrid vision transformer and data uncertainty learning
Feature embeddings derived from continuous mapping using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …
critical for accurate classification in seizure prediction tasks. However, the embeddings of …
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 …