A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
Scalp EEG classification using deep Bi-LSTM network for seizure detection
X Hu, S Yuan, F Xu, Y Leng, K Yuan, Q Yuan - Computers in Biology and …, 2020 - Elsevier
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 …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
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 …
Machine learning-based EEG signals classification model for epileptic seizure detection
The detection of epileptic seizures by classifying electroencephalography (EEG) signals into
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
Optimizing epileptic seizure recognition performance with feature scaling and dropout layers
A Omar, T Abd El-Hafeez - Neural Computing and Applications, 2024 - Springer
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 …
Epileptic seizure prediction using deep transformer model
The electroencephalogram (EEG) is the most promising and efficient technique to study
epilepsy and record all the electrical activity going in our brain. Automated screening of …
epilepsy and record all the electrical activity going in our brain. Automated screening of …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning
The electroencephalogram (EEG) has been commonly used to identify epileptic seizures,
but identification of seizures from EEG remains a challenging task that requires qualified …
but identification of seizures from EEG remains a challenging task that requires qualified …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
EEG-Based Seizure detection using linear graph convolution network with focal loss
Y Zhao, C Dong, G Zhang, Y Wang, X Chen… - Computer methods and …, 2021 - Elsevier
Abstract Background and Objectives: Epilepsy is a clinical phenomenon caused by sudden
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …
abnormal and excessive discharge of brain neurons. It affects around 70 million people all …