AIMC Topic: Epilepsy

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Nonictal electroencephalographic measures for the diagnosis of functional seizures.

Epilepsia
OBJECTIVE: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by record...

A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals.

Sensors (Basel, Switzerland)
This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor ...

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

IEEE transactions on neural networks and learning systems
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classificati...

Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review.

Seizure
The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the C...

Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize epileptic tissue and improve epilepsy surgery outcome. We aimed to understand whether machine learning (ML) could complement ioECoG reading, how subgr...

Continual learning for seizure prediction via memory projection strategy.

Computers in biology and medicine
Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual scenarios where data changes over time. Catastrophic forgetting(CF) for learned electroencephalogram(EE...

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing m...

Generalizability of electroencephalographic interpretation using artificial intelligence: An external validation study.

Epilepsia
OBJECTIVE: The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, ...

Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
BACKGROUND: Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim to establish a prediction model via a combination of machine learnin...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Medical engineering & physics
Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for det...