IEEE journal of biomedical and health informatics
May 6, 2025
Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significant...
Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel approach for the automatic identification of epilepsy in EEG signals by incorporating advanced entropy-ba...
Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models hav...
Neural networks : the official journal of the International Neural Network Society
Apr 28, 2025
Electroencephalogram (EEG)-based brain network analysis has shown promise in brain disease research by revealing the complex connectivity among brain regions. However, existing methods struggle to fully utilize the large amounts of unlabeled data to ...
Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seizures. In many cases, epilepsy is associated with cognitive comorbidities that persist between seizur...
International journal of neural systems
Apr 28, 2025
Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years, significant progress has been made in this field. However, the predictive performance of existing ...
Developmental and epileptic encephalopathies (DEEs) are severe neurological disorders characterized by childhood-onset seizures and significant developmental impairments. Seizures are often refractory to treatment with traditional antiseizure medicat...
This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epilepti...
Epilepsy is a common neurological disorder characterized by a significant rate of disability. Accurate early diagnosis and precise localization of the epileptogenic zone are essential for timely intervention, seizure prevention, and personalized trea...
BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share pathophysiological changes remains poorly defined. In this study, we levera...
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