Review of machine and deep learning techniques in epileptic seizure detection using physiological signals and sentiment analysis
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people
worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were …
worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were …
Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network
Z Zhang, M Xiao, T Ji, Y Jiang, T Lin, X Zhou… - Frontiers in …, 2024 - frontiersin.org
Introduction Epilepsy is a global chronic disease that brings pain and inconvenience to
patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that …
patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that …
Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation
Z Zhang, T Ji, M Xiao, W Wang, G Yu, T Lin… - … Signal Processing and …, 2024 - Elsevier
Cross-patient automatic epileptic seizure detection through electroencephalogram (EEG) is
significant for clinical application and research. However, most automatic seizure detection …
significant for clinical application and research. However, most automatic seizure detection …
A Temporal Multi-view Fuzzy Classifier for Fusion Identification on Epileptic Brain Network
Z Xia, W Xue, J Zhai, T Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Brain networks are commonly used to identify cognitive neurobehavioral and brain
conscious disorders. Most of the studies on state networks focus on the characterization and …
conscious disorders. Most of the studies on state networks focus on the characterization and …
Novel deep learning framework for detection of epileptic seizures using EEG signals
Introduction Epilepsy is a chronic neurological disorder characterized by abnormal electrical
activity in the brain, often leading to recurrent seizures. With 50 million people worldwide …
activity in the brain, often leading to recurrent seizures. With 50 million people worldwide …
Detection of epileptic seizure using EEG signals analysis based on deep learning techniques
The brain neurons' electrical activities represented by Electroencephalogram (EEG) signals
are the most common data for diagnosing Epilepsy seizure, which is considered a chronic …
are the most common data for diagnosing Epilepsy seizure, which is considered a chronic …
New approaches to epileptic seizure prediction based on EEG signals using hybrid CNNs
This study employs the University of Bonn Dataset to address the importance of frequency
information in EEG data and introduces a methodology utilising the short-time Fourier …
information in EEG data and introduces a methodology utilising the short-time Fourier …
An intelligent mobile application to classify employee mental workload based on EEG dataset using machine learning
S Gamage - 2024 - oulurepo.oulu.fi
This thesis explored a novel approach to assess mental workload (MWL) by combining
machine learning (ML) and electroencephalography (EEG) data. Aiming for a more objective …
machine learning (ML) and electroencephalography (EEG) data. Aiming for a more objective …
Epileptic Seizure Detection and Classification Based on EEG Signals Using Particle Swarm Optimization and Whale Optimization Algorithm
P Dhar, VK Garg - Computer Science Engineering and Emerging …, 2024 - taylorfrancis.com
Researchers claim that as the population has grown, more people are affected by epilepsy
than ever before—about 65 million people, according to a WHO report …
than ever before—about 65 million people, according to a WHO report …