Complex networks and deep learning for EEG signal analysis
Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …
representation of the human's physiological and pathological states. Up to now, much work …
Automated epilepsy detection techniques from electroencephalogram signals: a review study
Epilepsy is a serious neurological condition which contemplates as top 5 reasons for
avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy …
avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy …
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 …
Weighted visibility graph with complex network features in the detection of epilepsy
Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of
undergone subject is a challenge task for both research and neurologist due to the non …
undergone subject is a challenge task for both research and neurologist due to the non …
Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach
Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task
due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected …
due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected …
Epileptic seizure classification of EEGs using time–frequency analysis based multiscale radial basis functions
The automatic detection of epileptic seizures from electroencephalography (EEG) signals is
crucial for the localization and classification of epileptic seizure activity. However, seizure …
crucial for the localization and classification of epileptic seizure activity. However, seizure …
Epilepsy detection from EEG using complex network techniques: A review
Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-
third of epileptic patients experience seizures attack even with medicated treatment. The …
third of epileptic patients experience seizures attack even with medicated treatment. The …
Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals
Objective: Synchronization phenomena of epileptic electroencephalography (EEG) have
long been studied. In this study, we aim at investigating the spatial-temporal synchronization …
long been studied. In this study, we aim at investigating the spatial-temporal synchronization …
Finite-Time Synchronization and Synchronization of Multiweighted Complex Networks With Adaptive State Couplings
In this paper, two kinds of multiweighted and adaptive state coupled complex networks
(CNs) with or without coupling delays are presented. First, we develop the appropriate state …
(CNs) with or without coupling delays are presented. First, we develop the appropriate state …
Neurological abnormality detection from electroencephalography data: a review
The efficient detection of neurological abnormalities (disorders) is very important in clinical
diagnosis for modern medical applications. As stated by the World Health Organization's …
diagnosis for modern medical applications. As stated by the World Health Organization's …