[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
Automated EEG analysis of epilepsy: a review
Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures.
Electroencephalogram (EEG) is a test that measures and records the electrical activity of the …
Electroencephalogram (EEG) is a test that measures and records the electrical activity of the …
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of
epilepsy. The EEG signal contains information about the electrical activity of the brain …
epilepsy. The EEG signal contains information about the electrical activity of the brain …
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 …
Scalp EEG classification using deep Bi-LSTM network for seizure detection
Automatic seizure detection technology not only reduces workloads of neurologists for
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers
Electroencephalogram (EEG) comprises valuable details related to the different
physiological state of the brain. In this paper, a framework is offered for detecting the …
physiological state of the brain. In this paper, a framework is offered for detecting the …
Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform
A system for epileptic seizure detection in electroencephalography (EEG) is described in this
paper. One of the challenges is to distinguish rhythmic discharges from nonstationary …
paper. One of the challenges is to distinguish rhythmic discharges from nonstationary …
Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
Epileptic seizure is the most common disorder of human brain, which is generally detected
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
Automated diagnosis of epileptic EEG using entropies
Epilepsy is a neurological disorder characterized by the presence of recurring seizures. Like
many other neurological disorders, epilepsy can be assessed by the electroencephalogram …
many other neurological disorders, epilepsy can be assessed by the electroencephalogram …
Spiking neural networks
S Ghosh-Dastidar, H Adeli - International journal of neural systems, 2009 - World Scientific
Most current Artificial Neural Network (ANN) models are based on highly simplified brain
dynamics. They have been used as powerful computational tools to solve complex pattern …
dynamics. They have been used as powerful computational tools to solve complex pattern …