AIMC Topic: Epilepsy

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Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

Scientific reports
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogeni...

Robot-assisted, real-time, MRI-guided laser interstitial thermal therapy for pediatric patients with hypothalamic hamartoma: surgical technique, pitfalls, and initial results.

Journal of neurosurgery. Pediatrics
OBJECTIVE: Real-time, MRI-guided laser interstitial thermal therapy (MRgLITT) has been reported as a safe and effective technique for the treatment of epileptogenic foci in children and adults. After the recent approval of MRgLITT by the European Med...

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

Journal of healthcare engineering
OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.

Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

Epilepsia
OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) electroencephalography (EEG) recordings.

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both an...

Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks.

PloS one
We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with re...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signa...

Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach.

Computational and mathematical methods in medicine
The precise detection of epileptic seizure helps to prevent the serious consequences of seizures. As the electroencephalogram (EEG) reflects the brain activity of patients effectively, it has been widely used in epileptic seizure detection in the pas...

A simulation study to investigate the use of concentric tube robots for epilepsy surgery.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Patients with pharmacoresistant refractory epilepsy may require epilepsy surgery to prevent future seizure occurrences. Conventional surgery consists of a large craniotomy with straight rigid tools with associated outcomes of morbidity, larg...

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.

Journal of biomedical semantics
BACKGROUND: Drug repurposing can improve the return of investment as it finds new uses for existing drugs. Literature-based analyses exploit factual knowledge on drugs and diseases, e.g. from databases, and combine it with information from scholarly ...