AIMC Topic: Epilepsy

Clear Filters Showing 311 to 320 of 424 articles

Deep learning for detection of focal epileptiform discharges from scalp EEG recordings.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promising novel approach, being able to learn from large datasets. Here, we show pilot results of detecting...

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for mod...

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network.

IEEE transactions on bio-medical engineering
OBJECTIVE: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for di...

Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals.

Journal of medical engineering & technology
Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynami...

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

Journal of neural engineering
OBJECTIVE: Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usabil...

Encoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex.

Neuron
When speaking, we dynamically coordinate movements of our jaw, tongue, lips, and larynx. To investigate the neural mechanisms underlying articulation, we used direct cortical recordings from human sensorimotor cortex while participants spoke natural ...

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...

Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in deep learning are able to capture high-quality knowledge from textual data and represent it in graphi...

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

Epilepsia
OBJECTIVE: Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In ...

Epileptic seizure detection in EEG signal using machine learning techniques.

Australasian physical & engineering sciences in medicine
Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are ti...