A deep learning based ensemble learning method for epileptic seizure prediction
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is …
Artificial intelligence in epilepsy—applications and pathways to the clinic
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy
have increased exponentially over the past decade. Integration of AI into epilepsy …
have increased exponentially over the past decade. Integration of AI into epilepsy …
Focal and non-focal epilepsy localization: A review
AF Hussein, N Arunkumar, C Gomes… - IEEE …, 2018 - ieeexplore.ieee.org
The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …
has affected million people in the world. Hence, an early detection of the focal epileptic …
FFT-based deep feature learning method for EEG classification
M Li, W Chen - Biomedical Signal Processing and Control, 2021 - Elsevier
This study introduces a new method for electroencephalogram (EEG) signal classification
based on deep learning model, by which relevant features are automatically learned in a …
based on deep learning model, by which relevant features are automatically learned in a …
The ictal signature of thalamus and basal ganglia in focal epilepsy: a SEEG study
Objective To determine the involvement of subcortical regions in human epilepsy by
analyzing direct recordings from these regions during epileptic seizures using stereo-EEG …
analyzing direct recordings from these regions during epileptic seizures using stereo-EEG …
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 …
Detection of epileptic seizure based on entropy analysis of short-term EEG
Entropy measures that assess signals' complexity have drawn increasing attention recently
in biomedical field, as they have shown the ability of capturing unique features that are …
in biomedical field, as they have shown the ability of capturing unique features that are …
Epileptic seizure prediction based on permutation entropy
Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all
ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary …
ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary …
Fuzzy distribution entropy and its application in automated seizure detection technique
T Zhang, W Chen, M Li - Biomedical Signal Processing and Control, 2018 - Elsevier
Visual inspection of Electroencephalogram (EEG) records is the conventional diagnostic
method of epilepsy but it is expensive, time-consuming and tedious. Therefore, it is …
method of epilepsy but it is expensive, time-consuming and tedious. Therefore, it is …
Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis
Stroke survivors are more prone to developing cognitive impairment and dementia.
Dementia detection is a challenge for supporting personalized healthcare. This study …
Dementia detection is a challenge for supporting personalized healthcare. This study …