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Seizures

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AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Deep learning provides an appealing solution for the ongoing challenge of automatically classifying intracranial interictal epileptiform discharges (IEDs). We report results from an automated method consisting of a template-matching algori...

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Scientific reports
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive A...

Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis.

Journal of pharmacological and toxicological methods
Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning tech...

Data-driven electrophysiological feature based on deep learning to detect epileptic seizures.

Journal of neural engineering
. To identify a new electrophysiological feature characterising the epileptic seizures, which is commonly observed in different types of epilepsy.. We recorded the intracranial electroencephalogram (iEEG) of 21 patients (12 women and 9 men) with mult...

Deep learning based smart health monitoring for automated prediction of epileptic seizures using spectral analysis of scalp EEG.

Physical and engineering sciences in medicine
Being one of the most prevalent neurological disorders, epilepsy affects the lives of patients through the infrequent occurrence of spontaneous seizures. These seizures can result in serious injuries or unexpected deaths in individuals due to acciden...

Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine.

Biomedizinische Technik. Biomedical engineering
Seizures, the main symptom of epilepsy, are provoked due to a neurological disorder that underlies the disease. The accurate detection of seizures is a crucial step in any procedure of treatment. In the present study, electrocorticogram (ECoG) signal...

Video-Based Detection of Generalized Tonic-Clonic Seizures Using Deep Learning.

IEEE journal of biomedical and health informatics
Timely detection of seizures is crucial to implement optimal interventions, and may help reduce the risk of sudden unexpected death in epilepsy (SUDEP) in patients with generalized tonic-clonic seizures (GTCSs). While video-based automated seizure de...

Can machine learning improve randomized clinical trial analysis?

Seizure
PURPOSE: Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs). RCTs suffer from high costs and statistical inefficiencies. Using realistic simulation and machine ...

A deep learning based ensemble learning method for epileptic seizure prediction.

Computers in biology and medicine
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before t...

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

Annals of neurology
OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients.