Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.

The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve ...

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information.

Efficient processing of large-scale time-series data is an intricate problem in machine learning. Co...

Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes.

Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in...

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.

Emotion recognition is a key attribute for realizing advances in human-computer interaction, especia...

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

With modern population growth and an increase in the average lifespan, more patients are becoming af...

Completion of disconnective surgery for refractory epilepsy in pediatric patients using robot-assisted MRI-guided laser interstitial thermal therapy.

OBJECTIVE: Since 2007, the authors have performed 34 hemispherotomies and 17 posterior quadrant disc...

A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG)...

Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine le...

Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG.

. Auditory attention decoding (AAD) determines which speaker the listener is focusing on by analyzin...

Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence.

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as ...

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Humans experience a variety of emotions throughout the course of their daily lives, including happin...

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapi...

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrat...

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and compute...

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain an...

Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline.

Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared ...

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interict...

Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.

The development of validated algorithms for automated handling of artifacts is essential for reliabl...

Three simple steps to improve the interpretability of EEG-SVM studies.

Machine-learning systems that classify electroencephalography (EEG) data offer important perspective...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) lo...

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