AIMC Topic: Drug Resistant Epilepsy

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Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging seizure activities from electroencephalogram (EEG) recordings in drug-res...

Machine learning models for predicting treatment response in infantile epilepsies.

Epilepsy & behavior : E&B
UNLABELLED: Epilepsy stands as one of the prevalent and significant neurological disorders, representing a critical healthcare challenge. Recently, machine learning techniques have emerged as versatile tools across various healthcare domains, encompa...

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Scientific reports
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
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
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...

Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

Acta neurochirurgica
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
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
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...