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Epilepsy

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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...

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...

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Journal of neural engineering
Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time ...

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

International journal of neural systems
Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free ...

Universum based Lagrangian twin bounded support vector machine to classify EEG signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram (EEG) signals which contain noisy signals and outliers. Universum data contain...

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

Biosensors
The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, smal...

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

International journal of environmental research and public health
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches i...

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

International journal of neural systems
Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using ...

Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns.