Application of entropies for automated diagnosis of epilepsy using EEG signals: A review
Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …
Seizure prediction: the long and winding road
The sudden and apparently unpredictable nature of seizures is one of the most disabling
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …
aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures …
Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network
Epilepsy seizure prediction paves the way of timely warning for patients to take more active
and effective intervention measures. Compared to seizure detection that only identifies the …
and effective intervention measures. Compared to seizure detection that only identifies the …
Focal onset seizure prediction using convolutional networks
H Khan, L Marcuse, M Fields… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …
Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system
Recognition of epileptic seizures from offline EEG signals is very important in clinical
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …
Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach
Epileptic seizures occur as a result of a process that develops over time and space in
epileptic networks. In this study, we aim at developing a generalizable method for patient …
epileptic networks. In this study, we aim at developing a generalizable method for patient …
Classification of seizure and nonseizure EEG signals using empirical mode decomposition
V Bajaj, RB Pachori - IEEE Transactions on Information …, 2011 - ieeexplore.ieee.org
In this paper, we present a new method for classification of electroencephalogram (EEG)
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
signals using empirical mode decomposition (EMD) method. The intrinsic mode functions …
Epileptic seizure prediction using relative spectral power features
M Bandarabadi, CA Teixeira, J Rasekhi… - Clinical …, 2015 - Elsevier
Objective Prediction of epileptic seizures can improve the living conditions for refractory
epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and …
epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and …
Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power
Z Zhang, KK Parhi - IEEE transactions on biomedical circuits …, 2015 - ieeexplore.ieee.org
Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense
stationary and change from seizure to seizure, electrode to electrode, and from patient to …
stationary and change from seizure to seizure, electrode to electrode, and from patient to …