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
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 …
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 …
Classification of seizure and nonseizure EEG signals using empirical mode decomposition
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 …
Exploring the applicability of transfer learning and feature engineering in epilepsy prediction using hybrid transformer model
Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
Epileptic seizure prediction using relative spectral power features
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 …