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Epilepsy

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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...

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

IEEE transactions on medical imaging
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, t...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

Journal of neurosurgery
OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs b...

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Computers in biology and medicine
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood...

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI.

NeuroImage. Clinical
Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resecti...

A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Scientific reports
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a dif...

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

Seizure
OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).