Neurology

Seizures

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

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Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.

Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly us...

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to ...

Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

This paper puts forward a new method of landscape recognition and evaluation by using aerial video a...

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Brain-computer interface (BCI) aims to establish communication paths between the brain processes and...

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA mod...

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Automatic high-level feature extraction has become a possibility with the advancement of deep learni...

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis i...

Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks.

Color has an important role in object recognition and visual working memory (VWM). Decoding color VW...

Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study.

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal scleros...

Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical ...

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals.

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnos...

A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals.

This study presents a method with high accuracy performance that aims to automatically detect schizo...

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis.

Horizon scanning for innovative technologies that might be applied to medical products and requires ...

An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition.

Domain adaptation (DA) tackles the problem where data from the source domain and target domain have ...

Deep learning-based diagnosis of temporal lobe epilepsy associated with hippocampal sclerosis: An MRI study.

PURPOSE: The currently available indicators-sensitivity and specificity of expert radiological evalu...

DNA methylation-based classification of malformations of cortical development in the human brain.

Malformations of cortical development (MCD) comprise a broad spectrum of structural brain lesions fr...

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

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (...

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based ...

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

OBJECTIVE: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for a...

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

The ability to forecast seizures minutes to hours in advance of an event has been verified using inv...

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