A review on machine learning for EEG signal processing in bioengineering
MP Hosseini, A Hosseini, K Ahi - IEEE reviews in biomedical …, 2020 - ieeexplore.ieee.org
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
Automated seizure prediction
In the past two decades, significant advances have been made on automated
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number …
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 …
Detection of epileptic seizure using pretrained deep convolutional neural network and transfer learning
Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the
attending physician. However, the human factor may cause erroneous diagnosis in the …
attending physician. However, the human factor may cause erroneous diagnosis in the …
Automated depression detection using deep representation and sequence learning with EEG signals
Depression affects large number of people across the world today and it is considered as
the global problem. It is a mood disorder which can be detected using …
the global problem. It is a mood disorder which can be detected using …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Epileptic seizure prediction using deep transformer model
The electroencephalogram (EEG) is the most promising and efficient technique to study
epilepsy and record all the electrical activity going in our brain. Automated screening of …
epilepsy and record all the electrical activity going in our brain. Automated screening of …
Pediatric seizure prediction in scalp EEG using a multi-scale neural network with dilated convolutions
Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of
great significance for improving the quality of life of patients with epilepsy. In recent years, a …
great significance for improving the quality of life of patients with epilepsy. In recent years, a …
Automatic seizure detection using fully convolutional nested LSTM
The automatic seizure detection system can effectively help doctors to monitor and diagnose
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
epilepsy thus reducing their workload. Many outstanding studies have given good results in …
Dynamic learning framework for epileptic seizure prediction using sparsity based EEG reconstruction with optimized CNN classifier
BP Prathaban, R Balasubramanian - Expert Systems with Applications, 2021 - Elsevier
Abstract The World Health Organization (WHO) recently stated that epilepsy affects nearly
65 million people of the world population. Early forecast of the oncoming seizures is of …
65 million people of the world population. Early forecast of the oncoming seizures is of …