Seizures

Prediction error connectivity: A new method for EEG state analysis.

Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions, we describe a novel method devised to predict dynamics and thus highlight abrupt changes marked by unpredictability. Based on a variable-order Markov...

What does a pain 'biomarker' mean and can a machine be taught to measure pain?

Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardware and software. We discuss the merits and limitations of electroencephalography (EEG) as the hardware component of a putative embodiment of the device...

Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.

OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this techniq...

Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.

BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and imp...

A multi-context learning approach for EEG epileptic seizure detection.

BACKGROUND: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow res...

Detecting abnormal electroencephalograms using deep convolutional networks.

OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm t...

Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals.

OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep ...

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep...

Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data.

Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. ...

Coarse-to-fine information integration in human vision.

Coarse-to-fine theories of vision propose that the coarse information carried by the low spatial frequencies (LSF) of visual input guides the integrat...

Effects of Jobelyn on Isoniazid-Induced Seizures, Biomarkers of Oxidative Stress and Glutamate Decarboxylase Activity in Mice.

INTRODUCTION: Isoniazid-induced seizure, often described as Status Epilepticus (SE), is an emergency condition characterized by repeated convulsive ep...

EEG may serve as a biomarker in Huntington's disease using machine learning automatic classification.

Reliable markers measuring disease progression in Huntington's disease (HD), before and after disease manifestation, may guide a therapy aimed at slow...

Identification of Hidden Sources by Estimating Instantaneous Causality in High-Dimensional Biomedical Time Series.

The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a bette...

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has show...

The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.

OBJECTIVE: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arre...

Therapeutic drug monitoring of levetiracetam in daily clinical practice: high-performance liquid chromatography versus immunoassay.

OBJECTIVES: Although levetiracetam presents an easy dosing and tolerability, therapeutic drug monitoring may be recommended in certain situations. Mea...

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction.

The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capa...

Seizure forecasting using single robust linear feature as correlation vector of seizure-like events in brain slices preparation in vitro.

Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic s...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art appro...

Automated seizure prediction.

In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection...