[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 …
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 …
A multi-view deep learning method for epileptic seizure detection using short-time fourier transform
With the advances in pervasive sensor technologies, physiological signals can be captured
continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic …
continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic …
CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features
Reliable prediction of epileptic seizures is of paramount importance in reducing the serious
consequences of seizures by detecting their onset and warning patients early enough to …
consequences of seizures by detecting their onset and warning patients early enough to …
[HTML][HTML] Epileptic seizure detection using cross-bispectrum of electroencephalogram signal
Purpose The automatic detection of epileptic seizures in EEG data from extended recordings
can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce …
can make an important contribution to the diagnosis of epilepsy as it can efficiently reduce …
Epileptic seizure focus detection from interictal electroencephalogram: a survey
MR Islam, X Zhao, Y Miao, H Sugano… - Cognitive neurodynamics, 2023 - Springer
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients
S Elgohary, S Eldawlatly… - 2016 IEEE conference on …, 2016 - ieeexplore.ieee.org
Predicting the occurrence of epileptic seizures can provide an enormous aid to epileptic
patients. This paper introduces a novel patient-specific method for seizure prediction applied …
patients. This paper introduces a novel patient-specific method for seizure prediction applied …
A multi-context learning approach for EEG epileptic seizure detection
Background Epilepsy is a neurological disease characterized by unprovoked seizures in the
brain. The recent advances in sensor technologies allow researchers to analyze the …
brain. The recent advances in sensor technologies allow researchers to analyze the …
Efficient frameworks for EEG epileptic seizure detection and prediction
Seizure detection and prediction are a very hot topics in medical signal processing due to
their importance in automatic medical diagnosis. This paper presents three efficient …
their importance in automatic medical diagnosis. This paper presents three efficient …
Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation …
Idiopathic epilepsy is characterized by generalized seizures with no apparent cause. One of
its main problems is the lack of biomarkers to monitor the evolution of patients. The only …
its main problems is the lack of biomarkers to monitor the evolution of patients. The only …