AIMC Topic: Seizures

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Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.

Sensors (Basel, Switzerland)
Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epil...

A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
OBJECTIVE: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate synthetic EEG data. Another obj...

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.

Scientific reports
The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this s...

Coherent false seizure prediction in epilepsy, coincidence or providence?

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to...

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...