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 novel quick seizure detection and localization through brain data mining on ECoG dataset
Epilepsy is a common neurological disorder, and epileptic seizure detection is a scientific
challenge since sometimes patient do not experience any alert. The objective of this …
challenge since sometimes patient do not experience any alert. The objective of this …
[HTML][HTML] Automated diagnosis of epileptic seizures using eeg image representations and deep learning
Background The identification of seizure and its complex waveforms in
electroencephalography (EEG) through manual examination is time consuming, tedious …
electroencephalography (EEG) through manual examination is time consuming, tedious …
[PDF][PDF] Integrated CWT-CNN for epilepsy detection using multiclass EEG dataset
Electroencephalography is a common clinical procedure to record brain signals generated
by human activity. EEGs are useful in Brain controlled interfaces and other intelligent …
by human activity. EEGs are useful in Brain controlled interfaces and other intelligent …
[PDF][PDF] Epileptic seizure detection using deep learning through min max scaler normalization
B Deepa, K Ramesh - Int. J. Health Sci, 2022 - pdfs.semanticscholar.org
Epileptic seizure detection and prediction are significantly sought-after research currently
because robust algorithms are available. Machine learning and deep learning have allowed …
because robust algorithms are available. Machine learning and deep learning have allowed …
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 …
Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
M Varlı, H Yılmaz - Journal of Computational Science, 2023 - Elsevier
Epilepsy stands out as one of the common neurological diseases. The neural activity of the
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
Detection of epileptic seizures using convolutional neural network
One of the most prevalent neurological ailments, Epilepsy, affects around 1-2% of the entire
population of earth. It is the second only of stroke when it comes to neurological sickness …
population of earth. It is the second only of stroke when it comes to neurological sickness …
AHW-BGOA-DNN: A novel deep learning model for epileptic seizure detection
Abstract “Brain–Computer Interface”(BCI)—a real-life support system provides a way for
epileptic patients to improve their quality of life. In general, epileptic seizure detection using …
epileptic patients to improve their quality of life. In general, epileptic seizure detection using …
[HTML][HTML] Eeg-based seizure detection using variable-frequency complex demodulation and convolutional neural networks
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable
seizures that affect millions of people around the world. Early and accurate epilepsy …
seizures that affect millions of people around the world. Early and accurate epilepsy …