Epileptic seizure detection using deep convolutional autoencoder
AM Abdelhameed, HG Daoud… - 2018 IEEE international …, 2018 - ieeexplore.ieee.org
Monitoring and recording brain activities using Electroencephalograms (EEGs) has become
the foremost wide applied tool by physicians for epilepsy diagnosis due to viable reasons …
the foremost wide applied tool by physicians for epilepsy diagnosis due to viable reasons …
Deep convolutional bidirectional LSTM recurrent neural network for epileptic seizure detection
AM Abdelhameed, HG Daoud… - 2018 16th IEEE …, 2018 - ieeexplore.ieee.org
Recording the brain electrical activities using Electroencephalogram (EEG) has become the
most widely applied tool by physicians for the diagnosis of neurological disorders. In this …
most widely applied tool by physicians for the diagnosis of neurological disorders. In this …
A deep learning scheme for automatic seizure detection from long-term scalp EEG
Epilepsy is a chronic brain disorder that is expressed by seizures. Monitoring brain activity
via electroencephalogram (EEG) is an established method for epilepsy diagnosis and for …
via electroencephalogram (EEG) is an established method for epilepsy diagnosis and for …
Semi-supervised EEG signals classification system for epileptic seizure detection
AM Abdelhameed, M Bayoumi - IEEE Signal Processing Letters, 2019 - ieeexplore.ieee.org
In the past few decades, measuring and recording the brain electrical activities using
Electroencephalogram (EEG) has become a standout amongst the tools utilized for …
Electroencephalogram (EEG) has become a standout amongst the tools utilized for …
Epileptic seizure detection for multi-channel EEG with deep convolutional neural network
C Park, G Choi, J Kim, S Kim, TJ Kim… - 2018 International …, 2018 - ieeexplore.ieee.org
A new epileptic seizure detection method based on deep convolutional network is proposed.
The proposed network is designed for multi-channel EEG signals and considers spatio …
The proposed network is designed for multi-channel EEG signals and considers spatio …
Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network
HG Daoud, AM Abdelhameed… - 2018 IEEE 14th …, 2018 - ieeexplore.ieee.org
Electroencephalogram (EEG) used to record the electrical activity of the brain is a standout
amongst the most helpful tools which are utilized in the diagnosis of neurological disorders …
amongst the most helpful tools which are utilized in the diagnosis of neurological disorders …
Automatic seizure detection via an optimized image-based deep feature learning
I Alkanhal, BVKV Kumar… - 2018 17th IEEE …, 2018 - ieeexplore.ieee.org
In this paper, our goal is to find an optimized approach that can learn features from
multichannel EEG time-series data to perform automatic seizure detection. In general, it is …
multichannel EEG time-series data to perform automatic seizure detection. In general, it is …
A Machine Learning Framework for Robust Epileptic Seizure Detection from EEG Sign
Millions of individuals throughout the world suffer from epilepsy, a neurological illness
characterized by frequent seizures. To improve patient safety and quality of life, seizure …
characterized by frequent seizures. To improve patient safety and quality of life, seizure …
Deep recurrent neural network for seizure detection
EEG is one the most effective tools used in the diagnosis of epilepsy. However, proper
diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long …
diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long …
EEG classification via convolutional neural network-based interictal epileptiform event detection
Diagnosis of epilepsy based on visual inspection of electroencephalogram (EEG)
abnormalities is an inefficient, time-consuming, and expert-centered process. Moreover, the …
abnormalities is an inefficient, time-consuming, and expert-centered process. Moreover, the …