… Imbalanced classification existing in seizure detection is addressed by weighted ELM. …
Therefore, the imbalanced classification is deployed to resolve the problem of seizure detection in …

… An imbalanced deep learning model is proposed in this paper to improve the performance
of seizure detection. To modify imbalanced EEG data distribution, a generative adversarial …

… be the best indicators of seizures, we aim to build an Artificial Neural Networks to classify
seizure and non-seizure events. However, the limited availability of seizure events in the EEG …

… have been used to classify epilepsy from … imbalanced. This work presents an integrated
machine learning approach for epilepsy detection, which can effectively learn from imbalanced …

… imbalance is defined as having more observations in majority class making it difficult for the
classifier in the detection … to solve class imbalance problems while detecting the seizure. In …

… seizure detection is class imbalance because of long hour EEG recordings. The imbalance
… Previous researchers have focused on seizure detection. Despite the EEG data set is highly …

… seizure detection problem. We propose a robust learning framework to mitigate class imbalance
in … has severe class imbalance, with average representation of 0.38% of seizure class to …

… , defined as a number of seizures in the brain that affect the … to identify epileptic seizures
through available datasets. This … and RNN for seizure detection in imbalanced dataset. In the …

… are imbalanced such as the Epileptic Seizure dataset used in this work. Class imbalance …
ELM) method was proposed for seizure detection with imbalanced EEG data distribution [20]…

… of non-seizure periods are much longer than those of seizure activities. An imbalanced
learning model is proposed in this paper to improve the identification of seizure events in long-…