AIMC Topic: Drug Resistant Epilepsy
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Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis.
Epilepsy & behavior : E&B
Sep 21, 2024
BACKGROUND AND OBJECTIVES: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.
Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.
Scientific reports
Sep 18, 2024
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to ha...
A data augmentation procedure to improve detection of spike ripples in brain voltage recordings.
Neuroscience research
Aug 3, 2024
Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For patients with drug-resistant epilepsy, treatments include neurostimulation or surgical removal of the epileptogenic zone (EZ), the brain region responsibl...
Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.
NeuroImage
Jun 14, 2024
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed...
Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias.
Epilepsy research
Apr 3, 2024
PURPOSE: Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD det...
Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.
Acta neurochirurgica
Feb 16, 2024
BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for drug-resistant epilepsies. The introduction of robotic devices provides a simplified, accurate, and safe alternative to the conventional SEEG techniqu...
Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.
NeuroImage
Dec 15, 2023
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...
Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience.
World neurosurgery
Nov 28, 2023
BACKGROUND: Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detaili...
Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.
NeuroImage
Sep 15, 2023
Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and th...
Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy.
Epilepsia open
Aug 29, 2023
OBJECTIVE: Normal interictal [ F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co-registered to MRI) can then be used to compare epilepsy pa...
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