AIMC Topic: Epilepsy
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CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection.
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...
Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.
Neuroscience
May 5, 2025
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...
Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.
Scientific reports
May 2, 2025
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...
Interictal network dysfunction and cognitive impairment in epilepsy.
Nature reviews. Neuroscience
Apr 28, 2025
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...
Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.
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 ...
EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA.
Scientific reports
Apr 24, 2025
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...
Challenges and Opportunities: Nanomaterials in Epilepsy Diagnosis.
ACS nano
Apr 23, 2025
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...
Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.
Epilepsy & behavior : E&B
Apr 17, 2025
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...
Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.
Revue neurologique
Apr 10, 2025
INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intelligence (AI) - has revolutionized the collection, storage, and analysis of medical data in neurology...
Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.
IEEE transactions on biomedical circuits and systems
Apr 2, 2025
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...
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