An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …

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 …

Internet of Things (IoT) in solar energy: a bibliometrics analysis and global publications trends

MN AlMallahi, W Al Nassan… - Energy Harvesting …, 2023 - spiedigitallibrary.org
With the modernization of cities, the concept of the Internet of Things (IoT) is gaining
popularity and becoming a vital source of smart developments. An added advantage of solar …

LightSeizureNet: A lightweight deep learning model for real-time epileptic seizure detection

S Qiu, W Wang, H Jiao - IEEE Journal of Biomedical and …, 2022 - ieeexplore.ieee.org
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where
ultra-low power wearable seizure detection devices are essential in such a system. The …

Artificial neural network model using short-term fourier transform for epilepsy seizure detection

F Barneih, N Nasir, O Alshaltone… - 2022 Advances in …, 2022 - ieeexplore.ieee.org
Epilepsy is a neurological illness that can strike anyone at any time in their life. However, a
person with epilepsy will experience frequent to uncommon seizures, resulting in death …

Interactive local and global feature coupling for EEG-based epileptic seizure detection

Y Zhao, D Chu, J He, M Xue, W Jia, F Xu… - … Signal Processing and …, 2023 - Elsevier
Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate
the progress of epilepsy diagnosis. Current seizure detection methods based on deep …

Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection

B Gao, J Zhou, Y Yang, J Chi, Q Yuan - Biocybernetics and Biomedical …, 2022 - Elsevier
Automatic seizure detection technology is of great significance to reduce workloads of
neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge …

Automatic seizure identification from EEG signals based on brain connectivity learning

Y Zhao, M Xue, C Dong, J He, D Chu… - … journal of neural …, 2022 - World Scientific
Epilepsy is a neurological disorder caused by brain dysfunction, which could cause
uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography …

Epileptic disorder detection of seizures using EEG signals

MK Alharthi, KM Moria, DM Alghazzawi, HO Tayeb - Sensors, 2022 - mdpi.com
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized
clinical approach for recording electrical activity in the brain. Although there are a number of …

Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model.

X Dong, Y Wen, D Ji, S Yuan, Z Liu… - International Journal of …, 2024 - europepmc.org
Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and
treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and …