Seizures

Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE.

Machine learning for MEG during speech tasks.

We consider whether a deep neural network trained with raw MEG data can be used to predict the age of children performing a verb-generation task, a monosyllable speech-elicitation task, and a multi-syllabic speech-elicitation task. Furthermore, we argue that the network makes predictions on the grounds of differences in speech development. Previous work has explored taking 'deep' neural networks (...

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach.

OBJECTIVE: Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on...

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze ...

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals.

Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the ment...

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or ea...

Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot.

BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to dis...

EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based o...

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach.

In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically se...

Alternative Diagnosis of Epilepsy in Children Without Epileptiform Discharges Using Deep Convolutional Neural Networks.

Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosi...

A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method.

Surface EEG (Electroencephalography) signal is vulnerable to interference due to its characteristics and sampling methods. So it is of great importanc...

An Intelligent Sleep Apnea Classification System Based on EEG Signals.

Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Ele...

Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention i...

Antiepileptic and anti-neuroinflammatory effects of red ginseng in an intrahippocampal kainic acid model of temporal lobe epilepsy demonstrated by electroencephalography.

BACKGROUND: Chronic inflammation can lower the seizure threshold and have influence on epileptogenesis. The components of red ginseng (RG) have anti-i...

Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria.

The objective of this study was to test statistical features from the electroencephalogram (EEG) recordings as predictors of neurodevelopment and cogn...

From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.

OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system...

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework.

Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the ...

Automatic seizure detection using three-dimensional CNN based on multi-channel EEG.

BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients...

Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.

Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task...

High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology.

Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and dis...