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 …
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in
combination with deep learning computational methods has received much attention in …
combination with deep learning computational methods has received much attention in …
CNN based framework for detection of epileptic seizures
Epilepsy is a common neurological disease that uses electroencephalogram (EEG) data for
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
A deep convolutional neural network model for automated identification of abnormal EEG signals
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual
examination of these signals by experts is strenuous and time consuming. Hence, machine …
examination of these signals by experts is strenuous and time consuming. Hence, machine …
Applications of artificial intelligence in automatic detection of epileptic seizures using EEG signals: A review
S Saminu, G Xu, S Zhang… - Artificial Intelligence …, 2023 - ojs.bonviewpress.com
Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a
tedious and time-consuming task that may take several years of manual training due to its …
tedious and time-consuming task that may take several years of manual training due to its …
A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach
The brain electrical activity, recorded and materialized as electroencephalogram (EEG)
signals, is known to be very useful in the diagnosis of brain-related pathology. However …
signals, is known to be very useful in the diagnosis of brain-related pathology. However …
Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks
A Narin - Irbm, 2022 - Elsevier
Epilepsy is a neurological disease from which a large number of younger and older people
suffer all over the world. The status of the patients is primarily examined by using …
suffer all over the world. The status of the patients is primarily examined by using …
[HTML][HTML] Epileptic-net: an improved epileptic seizure detection system using dense convolutional block with attention network from EEG
Epilepsy is a complex neurological condition that affects a large number of people
worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and …
worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and …
[HTML][HTML] An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy
W Chen, Y Wang, Y Ren, H Jiang, G Du… - BMC Medical informatics …, 2023 - Springer
Background Epilepsy is a neurological disorder that is usually detected by
electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a …
electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a …
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