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 …
Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022)
An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …
and resource efficient tool for medical and healthcare applications. In automated …
An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods
M Shen, P Wen, B Song, Y Li - Biomedical Signal Processing and Control, 2022 - Elsevier
Epilepsy is one of the most common complex brain disorders which is a chronic non-
communicable disease caused by paroxysmal abnormal super-synchronous electrical …
communicable disease caused by paroxysmal abnormal super-synchronous electrical …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network
M Shen, P Wen, B Song, Y Li - Biomedical Signal Processing and Control, 2023 - Elsevier
Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons,
leading to transient brain dysfunctions. This paper proposed an EEG based real-time …
leading to transient brain dysfunctions. This paper proposed an EEG based real-time …
Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals
Recently, researchers in the biomedical community have introduced deep learning-based
epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate …
epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate …
A survey of brain network analysis by electroencephalographic signals
Brain network analysis is one efficient tool in exploring human brain diseases and can
differentiate the alterations from comparative networks. The alterations account for time …
differentiate the alterations from comparative networks. The alterations account for time …
Seizure types classification by generating input images with in-depth features from decomposed EEG signals for deep learning pipeline
Electroencephalogram (EEG) based seizure types classification has not been addressed
well, compared to seizure detection, which is very important for the diagnosis and prognosis …
well, compared to seizure detection, which is very important for the diagnosis and prognosis …
Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images
Epilepsy is a prevalent neurological disorder with considerable risks, including physical
impairment and irreversible brain damage from seizures. Given these challenges, the …
impairment and irreversible brain damage from seizures. Given these challenges, the …
The performance evaluation of the state-of-the-art EEG-based seizure prediction models
Z Ren, X Han, B Wang - Frontiers in Neurology, 2022 - frontiersin.org
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and
even death. The rapid development of electroencephalogram (EEG) and Artificial …
even death. The rapid development of electroencephalogram (EEG) and Artificial …