[Retracted] Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health.
It occurs abruptly without any symptoms and thus increases the mortality rate of humans …
It occurs abruptly without any symptoms and thus increases the mortality rate of humans …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning
The ability to forecast seizures minutes to hours in advance of an event has been verified
using invasive EEG devices, but has not been previously demonstrated using noninvasive …
using invasive EEG devices, but has not been previously demonstrated using noninvasive …
Theoretical and methodological analysis of EEG based seizure detection and prediction: An exhaustive review
Epilepsy is a chronic neurological disorder with a comparatively high prevalence rate. It is a
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
condition characterized by repeated and unprovoked seizures. Seizures are managed with …
Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models
The development of seizure prediction models is often based on long-term scalp
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …
electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive …
Automatic electroencephalogram artifact removal using deep convolutional neural networks
Scalp electroencephalogram (EEG) is a non-invasive measure of brain activity. It is widely
used in several applications including cognitive tasks, sleep stage detection, and seizure …
used in several applications including cognitive tasks, sleep stage detection, and seizure …
Seizure detection and prediction by parallel memristive convolutional neural networks
During the past two decades, epileptic seizure detection and prediction algorithms have
evolved rapidly. However, despite significant performance improvements, their hardware …
evolved rapidly. However, despite significant performance improvements, their hardware …
Weak self-supervised learning for seizure forecasting: a feasibility study
This paper proposes an artificial intelligence system that continuously improves over time at
event prediction using initially unlabelled data by using self-supervised learning. Time …
event prediction using initially unlabelled data by using self-supervised learning. Time …
Instrumentation, measurement, and signal processing in electroencephalography-based brain–computer interfaces: situations and prospects
Z Xue, Y Zhang, H Li, H Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Proper signal measurement and processing are crucial in electroencephalography (EEG)-
based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise …
based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise …
The goal of explaining black boxes in EEG seizure prediction is not to explain models' decisions
Many state‐of‐the‐art methods for seizure prediction, using the electroencephalogram, are
based on machine learning models that are black boxes, weakening the trust of clinicians in …
based on machine learning models that are black boxes, weakening the trust of clinicians in …