EEG based multi-class seizure type classification using convolutional neural network and transfer learning
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the
cortical connectivity of the brain. Though automated early recognition of seizures from …
cortical connectivity of the brain. Though automated early recognition of seizures from …
A deep learning approach for automatic seizure detection in children with epilepsy
A Abdelhameed, M Bayoumi - Frontiers in Computational …, 2021 - frontiersin.org
Over the last few decades, electroencephalogram (EEG) has become one of the most vital
tools used by physicians to diagnose several neurological disorders of the human brain and …
tools used by physicians to diagnose several neurological disorders of the human brain and …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
Artificial intelligence in epilepsy
Background: The study of seizure patterns in electroencephalography (EEG) requires
several years of intensive training. In addition, inadequate training and human error may …
several years of intensive training. In addition, inadequate training and human error may …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection
K Akyol - Expert Systems with Applications, 2020 - Elsevier
Electroencephalography signals obtained from the brain's electrical activity are commonly
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
used for the diagnosis of neurological diseases. These signals indicate the electrical activity …
Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners
Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The
phase-space representation (PSR) method is useful for analysing the non-linear …
phase-space representation (PSR) method is useful for analysing the non-linear …
Optimizing epileptic seizure recognition performance with feature scaling and dropout layers
Epilepsy is a widespread neurological disorder characterized by recurring seizures that
have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is …
have a significant impact on individuals' lives. Accurately recognizing epileptic seizures is …
Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications
Epileptic seizures are characterised by abnormal neuronal discharge, causing notable
disturbances in electrical activities of the human brain. Traditional methods based on …
disturbances in electrical activities of the human brain. Traditional methods based on …