AIMC Topic: Epilepsy

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A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

IEEE journal of biomedical and health informatics
Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the eff...

Characterizing physiological high-frequency oscillations using deep learning.

Journal of neural engineering
Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation...

A comparison between robot-guided and stereotactic frame-based stereoelectroencephalography (SEEG) electrode implantation for drug-resistant epilepsy.

Journal of robotic surgery
The original stereoelectroencephalography frame-based implantation technique has been proven to be safe and effective. But this procedure is complicated and time-consuming. With the development of modern robotic technology, robot-guided intracerebral...

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Scientific reports
Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challengi...

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.

IEEE journal of biomedical and health informatics
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortic...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BMC medical informatics and decision making
BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance...

Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for au...

Completion of disconnective surgery for refractory epilepsy in pediatric patients using robot-assisted MRI-guided laser interstitial thermal therapy.

Journal of neurosurgery. Pediatrics
OBJECTIVE: Since 2007, the authors have performed 34 hemispherotomies and 17 posterior quadrant disconnections (temporoparietooccipital [TPO] disconnections) for refractory epilepsy at Sant Joan de Déu Barcelona Children's Hospital. Incomplete discon...

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

IEEE transactions on biomedical circuits and systems
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal...

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

IEEE journal of biomedical and health informatics
Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals am...